Self-building hierarchically indexed multimedia database

ABSTRACT

Methods, apparatus, and systems for a self-building hierarchically indexed multimedia database are disclosed. The database includes multiple branches categorizing industries. Each branch supports at least one node tree associated with at least one issuer entity and stores multimedia content associated with the at least one issuer entity. A first pattern is extracted from a first node tree supported by a first branch using a machine learning module trained based on the database. A second pattern is extracted from a second node tree supported by a second branch. The first node tree includes at least one node more than the second node tree. It is determined that the first pattern matches the second pattern using the machine learning module. The machine learning module is trained to compare two patterns extracted from the database. A new node corresponding to the at least one node is incorporated within the second node tree.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.17/198,158, filed Mar. 10, 2021, now issued U.S. Pat. No. 11,210,596,which claims the benefit of U.S. Provisional Patent Application No.63/110,540, filed Nov. 6, 2020, which are all incorporated by referencein their entireties herein.

FIELD OF THE INVENTION

This description relates generally to multimedia databases andspecifically to self-building hierarchically indexed multimediadatabases.

BACKGROUND

A product and service-hierarchy database can organize comparableindustry, sector, subsector, and group market performance and stockinvestment information centered around products produced and servicesperformed by each company and its competitors, with each product orservice type created as an index. Such product hierarchy enables thecreation of an index for each product or service type that can be valuedand measured. The product hierarchy database organizes and trackscompany market performance and stock investment information by theproducts and services produced and offered by each competitor.

The product hierarchy is created in the database independently of thecompanies. The companies that produce each product are relationallylinked to each product in the hierarchy that corresponds to a productproduced or service performed by each company. An investment informationservice includes the product hierarchy database and makes it accessibleto investor and analyst subscribers through a query system across theInternet. Data entry personnel load qualitative and quantitativeinformation about companies and their products through a producthierarchy generator connected to the product hierarchy database.Subscribers can punch-through to query individual data items, and theycan find out what relationships exist between important aspects of thecompanies and the products being tracked. Such a database also providesperformance criteria by industry, sector, sub-sector, and group, therebyallowing industry, sector, sub-sector, and group-based qualitativeassessment.

SUMMARY

Methods, apparatus, and systems for self-building hierarchically indexedmultimedia databases are disclosed. In some embodiments, a computersystem traverses a hierarchically indexed multimedia database thatincludes multiple branches categorizing multiple industries. Each branchof the hierarchically indexed multimedia database supports at least onenode tree associated with at least one issuer entity. The node treestores multimedia content associated with multiple issuer entities. Thecomputer system extracts a first pattern from a first node treesupported by a first branch of the hierarchically indexed multimediadatabase using a machine learning module. The machine learning module istrained based on the hierarchically indexed multimedia database. Thecomputer system extracts a second pattern from a second node treesupported by a second branch of the hierarchically indexed multimediadatabase using the machine learning module. The first node tree includesat least one node more than the second node tree. The computer systemdetermines that the first pattern matches the second pattern using themachine learning module. The machine learning module is trained tocompare two patterns extracted from the hierarchically indexedmultimedia database. Responsive to determining that the first patternmatches the second pattern, the computer system incorporates a new nodewithin the second node tree in accordance with the first pattern.

In some embodiments, a computer system receives a request to modify anode of a hierarchically indexed multimedia database categorizingmultiple issuer entities. The hierarchically indexed multimedia databaseincludes at least one branch associated with a respective industry andsupports a node tree including the node to be modified. The computersystem extracts features indicative of a priority of the request. Thefeatures are extracted from the request, other requests received tomodify the node, and a structure of the hierarchically indexedmultimedia database. The computer system determines the priority of therequest based on the features using a machine learning module. Themachine learning module is trained based on the structure of thehierarchically indexed multimedia database and the other requests. Thecomputer system positions the request within the other requests based onthe priority. The computer system modifies the node tree with respect tothe structure of the hierarchically indexed multimedia database.

In some embodiments, the computer system generates a hierarchicallyindexed multimedia database categorizing multiple issuer entities. Thehierarchically indexed multimedia database includes at least one branchassociated with a respective industry. The branch supports at least onenode. The computer system extracts metadata, using a machine learningmodule, from multimedia content received from an issuer entity. Thecomputer system identifies the node, using the machine learning module,based on the metadata. The computer system stores the multimedia contentat the node, such that the multimedia content is associated with therespective industry and the issuer entity.

In some embodiments, a computer system receives multimedia content froma particular issuer entity. Multiple issuer entities are categorized bya hierarchically indexed multimedia database stored by a multimediacontent host. The hierarchically indexed multimedia database includes atleast one node referencing the particular issuer entity. The computersystem mines at least one analytics website using a machine learningmodule to identify a change in a rating of the particular issuer entity.The computer system traverses the hierarchically indexed multimediadatabase using the machine learning module based on the multimediacontent to identify the node. The computer system transmits themultimedia content and the change in the rating of the particular issuerentity to the multimedia content host for storage at the node. Thecomputer system receives a universal resource locator (URL) from themultimedia content host referencing the multimedia content. In responseto receiving a combinatorial query from an investor entity requestingthe multimedia content, the computer system displays the multimediacontent and the change in the rating of the particular issuer entity.

In some embodiments, a computer system determines a first metricquantifying user engagement with multimedia content stored at a node ofa hierarchically indexed multimedia database. The multimedia content andthe node are each associated with an issuer entity. The computer systemdetermines a second metric quantifying social media engagement,communication network activity, a trading volume, and a stock valueassociated with the issuer entity. The computer system determines amultidimensional correlation of the first metric to the second metric.The computer system ranks the issuer entity among the multiple issuerentities based on the multidimensional correlation. The computer systemupdates the node to include data describing a rank of the issuer entityamong the multiple issuer entities.

In some embodiments, a computer system mines the Internet for multimediacontent associated with multiple industries using a machine learningmodel. The multiple industries are categorized by a hierarchicallyindexed multimedia database including a particular branch associatedwith the particular industry. The computer system clusters themultimedia content among multiple issuer entities using deep learning.The deep learning is configured to determine a relationship from themultimedia content between each issuer entity and each other issuerentity. The computer system generates a node tree structured inaccordance with the relationship between each issuer entity and eachother issuer entity. The computer system incorporates the node treewithin the hierarchically indexed multimedia database, such that thenode tree is supported by the particular branch.

These and other aspects, features, and implementations can be expressedas methods, apparatus, systems, components, program products, means orsteps for performing a function, and in other ways.

These and other aspects, features, and implementations will becomeapparent from the following descriptions, including the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a public company analysis system,in accordance with one or more embodiments.

FIG. 2 is a flow diagram illustrating a process for cross indexing andautomated growth of a hierarchically indexed multimedia database, inaccordance with one or more embodiments.

FIG. 3 is a flow diagram illustrating hierarchy building, in accordancewith one or more embodiments.

FIG. 4 is a flow diagram illustrating media upload, storage, and hostingfor a self-building hierarchically indexed multimedia database, inaccordance with one or more embodiments.

FIG. 5 is a block diagram illustrating a system network architecture, inaccordance with one or more embodiments.

FIG. 6 is a flow diagram illustrating an example process for servingmedia on user query flow, in accordance with one or more embodiments.

FIG. 7 is a diagram illustrating an example graphical user interfacedisplaying a hierarchy dashboard for a self-building hierarchicallyindexed multimedia database, in accordance with one or more embodiments.

FIG. 8 is a diagram illustrating an example graphical user interfacedisplaying a hierarchy of a self-building hierarchically indexedmultimedia database, in accordance with one or more embodiments.

FIG. 9 is a flow diagram illustrating an example process forself-building hierarchically indexed multimedia databases, in accordancewith one or more embodiments.

FIG. 10 is a block diagram illustrating an example computer system, inaccordance with one or more embodiments.

FIG. 11 is a diagram illustrating an example graphical user interfacedisplaying an issuer profile questionnaire, in accordance with one ormore embodiments.

FIG. 12 is a diagram illustrating an example graphical user interfacedisplaying a media questionnaire, in accordance with one or moreembodiments.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present embodiments. It will be apparent, however,that the present embodiments may be practiced without these specificdetails.

This document presents methods, systems, and apparatuses forself-building hierarchically indexed multimedia databases. A product andservice-hierarchy database categorizes comparable industry, sector, andgroup market performance and stock investment information centeredaround the products produced and services performed by each company andits competitors. Examples of a product and service-hierarchy databaseare described in more detail in U.S. Pat. Nos. 6,338,067 and 6,405,204,each of which is incorporated herein in its entirety.

Introduced here are computer-implemented methods, apparatus, andcomputer systems for a self-building hierarchically indexed multimediadatabase that organizes multimedia content associated with issuerentities and tracks company market performance and stock investmentinformation by the products and services produced and offered.Embodiments include a hierarchically indexed and cross-indexed issuervideo and audio database, searchable by industry, sector, group,product, service, and company. Embodiments use combinatorial queries forvideo and audio search with unique qualitative criteria: globalgeography, filing status, shareholder meetings, analyst day, trading andreporting status, corporate actions, regulatory status, etc., andoverlay the search with standard quantitative criteria.

The embodiments generate video and audio correlated trading alerts basedupon viewing and listening activity and measure video and audiosentiment on the platform. Embodiments provide shared community inputand triggers to continue constructing and evolving an adaptive,automated, self-building database. In some embodiments of the invention,a hierarchy is built multidimensionally by supervised training, e.g., byan administrator, by unsupervised training, e.g., on the basis ofoperations performed on the back end by an issuer/company and by queryoperations performed by a user. For example, a user's queriesautomatically create a path in the hierarchy.

The hierarchy is organized in the self-building hierarchically indexedmultimedia database among multiple branches categorizing differentindustries, with at least one branch per industry. Each branch supportsa node tree of nodes associated with issuer entities. Multimedia contentassociated with an issuer entity is stored at one or more nodes. Thehierarchy can be implemented as a series of tables, with one table foreach level in the hierarchy. The tables contain references to allcategory levels above that level, with a direct reference to a parentnode. Additionally, each node in a level is assigned a “Primary Key” touniquely identify that node.

Automated self-building (e.g., add nodes, remove nodes, edit existingnodes, and manage relationships between existing nodes) using machinelearning is a key feature of the hierarchical system. The service systemuses a machine learning module to detect patterns within node trees anddetermines whether patterns match closely enough to replicate additionalnodes from one pattern to the other. For example, if “Branch A” (a chainof directly related nodes) contains four nodes, and three of the nodenames closely match (e.g., with greater than 98% word similarity) threenodes of the same relationship pattern in another industry's “Branch B,”the system would indicate adding the 4th node from Branch A to Branch Bin the same position.

The advantages and benefits of self-building hierarchically indexedmultimedia databases using the embodiments described herein includeincreased investment community visibility for public and privatecompanies compared to traditional databases. The embodiments providescalable, global, and cost-effective exposure for issuer entitiesutilizing the video and audio content and functionality provided by theembodiments. The multiple searchable attributes enable investor entitiesto readily find issuer entities, compared to traditional video/audioplatforms that are not issuer-specific. The self-building hierarchicallyindexed multimedia database drives investors, partners, and suitors toan issuer's business, website, crowdfunding platform, or tradedsecurity. For example, an issuer can communicate with buy-side,sell-side, and strategic partners, providing the issuer with investmentsponsorship, fundraising opportunities, and economically efficientaccess to investors.

The benefits and advantages for investor entities includes enabling aninvestor to conduct diligence on an issuer entity via various video andaudio content types using the functionality provided by the embodiments,e.g., company overview, product introduction, shareholder meetings,analyst day, etc. The enhanced functionality can be used to find issuersfor investment via a variety of searchable attributes, e.g., geography,filing status, industry, products, trading and reporting status,intellectual property (IP), headcount, etc. Diligence time and travelexpenses are reduced compared to traditional methods by displayingissuer video/audio. Moreover, video and audio alerts can be triggeredbased upon quantitative and qualitative factors. Enhanced communicationbetween company executives is achieved, giving investors, partners, andsuitors on-demand access to companies. In additional, the accuracy ofpeer group analysis and relative valuation comparisons based upon theembodiments is increased compared to traditional methods.

FIG. 1 is a block diagram illustrating a public company analysis system100, in accordance with one or more embodiments. The system 100 operatesover the Internet 102 and can support the securities investmentinformational needs of multiple investor entities (sometimes referred toas “investors”), represented in FIG. 1 as investor network clients 104,106, and 108. An investor refers to an entity (such as a firm or mutualfund) that commits capital to financial instruments to earn profits or arate of return. An issuer (sometimes referred to as an “issuer entity”)refers to an entity that develops, registers and sells securities.Issuers can be corporations, investment trusts, or domestic or foreigngovernments.

In some embodiments, a query manager 110 appears as a Web page andinterfaces the network clients 104, 106, and 108 with a producthierarchy database 112. Qualitative and quantitative information 114 and116 about public traded companies and their products are input through adata entry system 118 to a product hierarchy generator 120. Thequalitative and quantitative information 114 and 116 can come fromWeb-based research or traditional research based on documents andpublications. The product hierarchy generator 120 builds the database112 as a relational database that is structured by product or servicetype.

The database 112 is useful in the analysis of competing companies andtheir markets through the use of database relationships that are basedon product/service hierarchies. Investors are able to conductcomprehensive comparative valuation analysis by industry, sector,sub-sector, and group product and service. Investors can also obtainhierarchical industry, sector, sub-sector, and group profiles. Acombination of qualitative and quantitative data queries can besupported. Database 112 preferably allows investors to conduct queriesby searching on individual or multiple qualitative and quantitativecategories. Database 112 preferably allows investors to conductqualitative analysis of quantitative data and quantitative analysis ofqualitative data. Database 112 can be used in securities analysis ofpublicly traded or private companies and to increase partnershipinvestment performance.

The database 112 is an investment research database 112 that providesqualitative and quantitative data for publicly traded and privatecompanies in a single source accessible via the Internet. Database 112supports industry, sector, sub-sector, and group hierarchicalclassifications based on specific products or services. Queries throughthe Internet 102 allow investors to determine how specific companies arepositioned by group within a particular industry, sector, sub-sector, aswell as relative industry, sector, sub-sector, and group by industry,sector, sub-sector, and group performance.

In some embodiments, creation of industries, sectors, sub-sectors, andgroups, and the proper classification of companies enable comparativevaluation and peer group analysis. The product/service hierarchygenerator 120 categorizes companies into appropriate industries,sectors, sub-sectors, and groups, and product areas according to ahierarchy within their respective industries. In this way, investorusers can get peer group analysis, relative valuation comparisons, andqualitative queries within a chosen industry, sector, sub-sector, orgroup. The hierarchy is built based on products produced or servicesperformed within industries, which is a bottoms-up approach to companyclassification.

The embodiments disclosed herein implement the creation of industries,sectors, sub-sectors, and groups, and the proper classification ofcompanies in a hierarchically indexed multimedia database. Thehierarchically indexed multimedia database is the same as or similar tothe hierarchically indexed multimedia database 206 illustrated anddescribed in more detail with reference to FIG. 2. The hierarchicallyindexed multimedia database can learn and create and add industries,sectors, sub-sectors, and groups, and the proper classification ofcompanies to the hierarchy (product hierarchy database 112) usingsupervised training. The hierarchically indexed multimedia databasegenerates an index based upon industry, sector, node, product type, andservice type, especially where entries are cross indexed to otherwiseunrelated nodes within the hierarchy. Further, the hierarchicallyindexed multimedia database provides access to multimedia content(sometimes referred to as “media”), such as video corporatecommunications, podcasts, webcasts, and the like.

FIG. 2 is a flow diagram illustrating a process for cross indexing andautomated growth of a hierarchically indexed multimedia database 206, inaccordance with one or more embodiments. In some embodiments, theprocess 200 of FIG. 2 is performed by a computer system, e.g., theexample computer system 1000 illustrated and described in more detailwith reference to FIG. 10. Particular entities, for example, thehierarchically indexed multimedia database 206 itself or a host serviceperform some or all of the steps of the process in other embodiments.Likewise, embodiments may include different and/or additional steps, orperform the steps in different orders. The host service is the same asor similar to the host service 524 illustrated and described in moredetail with reference to FIG. 5.

In some embodiments, the computer system receives issuer profilequestionnaires describing multiple issuer entities. Each issuer profilequestionnaire is the same as or similar to the example issuer profilequestionnaire 1100, illustrated and described in more detail withreference to FIG. 11. An example of receiving an issuer profilequestionnaire is illustrated and described in more detail in step 402with reference to FIG. 4. The computer system generates thehierarchically indexed multimedia database 206 categorizing the multipleissuer entities based on the issuer profile questionnaires. Thehierarchically indexed multimedia database 206 includes at least onebranch associated with a respective industry and supports at least onenode. An example branch “Health Care” is illustrated and described inmore detail with reference to FIG. 8. Each node references at least oneissuer entity of the multiple issuer entities. An example node“Fertility Clinic” is illustrated and described in more detail withreference to FIG. 8.

In some embodiments, the issuer profile questionnaire includes metadatato provide that an issuer entity is uniquely identifiable within thesystem, and that when the issuer registers with the hierarchicallyindexed multimedia database 206, the computer system detects that theissuer already exists. The issuer will be able to recognize the existingcompany profile as belonging to it when the computer system displays theprofile. In some embodiments, audio/video file node associations in thehierarchically indexed multimedia database 206 are the same as or adescendant of issuer profile node associations. A node is not associatedbelow the minimum mandatory node level to an issuer. Once an issuer hasregistered and claimed an administratively created profile, its profileis not deleted.

In some embodiments, the graphical user interface for issuer profilecreation includes a new page for issuer creation, a new page for viewingexisting issuers in a table, a new page for editing/viewing issuerdetails, and a list of each issuer associated to a node in a “nodedetails page” and a button that redirects a user to the issuer creationpage within the node details page. The button autofills the associatednode ID in issuer creation process. In some embodiments, additionallogic for duplicate issuer creation and issuer deletion is included. Insome embodiments, a minimum amount of metadata is included in order touniquely distinguish a company that is entered into the hierarchicallyindexed multimedia database 206. The data fields can require specialformatting if needed, e.g., a Data Universal Numbering System (DUNS)number, founding date, headquarters, other uniquely identifying fields.A graphical user interface of the hierarchically indexed multimediadatabase 206 displays pre-selected nodes to an issuer as indicative ofanalyst requests. An issuer entity can thus remove a suggested nodeassociation or leave the request if appropriate.

In some embodiments, a group of a parent node, direct child node, anddirect grandchild node is called a branch. When two branches havesignificant naming or description overlap with another industry, across-index relationship is indicated between these branches. Branchescan be cross-indexed more than once. Branch sizes may vary, with largermatching branches having a stronger suggested correlation than smallerbranches. Competitors in company profiles across nodes/branches can bematched, e.g., when two companies in different nodes/industries claim tohave the same competitors, that indicates a relationship between thosenodes that could be weighted. Using the same weighting system as withweighting user requests, it is possible to weigh potential relationshipswithin the hierarchies and their relevance.

The computer system extracts metadata, using a machine learning module,from multimedia content received from each issuer entity. The machinelearning module is the same as or similar to the machine learning module518 illustrated and described in more detail with reference to FIG. 5.In some embodiments, the multimedia content includes video, audio, text,or encrypted data. For example, only authorized investor entities areallowed access to decrypt the data. The metadata is indicative of therespective industry. The computer system identifies, the node using themachine learning module, based on the metadata. The machine learningmodule is trained based on a structure of the hierarchically indexedmultimedia database 206, e.g., the structures shown in FIGS. 7 and 8.The computer system stores the multimedia content at the node, such thatthe multimedia content is associated with the respective industry and anissuer entity. In this manner, the computer system generates thehierarchically indexed multimedia database 206 categorizing multipleindustries and issuer entities.

In some embodiments, the computer system traverses the hierarchicallyindexed multimedia database 206, which includes multiple branchescategorizing multiple industries. Each branch supports at least one nodetree associated with at least one issuer entity and stores multimediacontent associated with the at least one issuer entity, as illustratedin more detail with reference to FIG. 8. The term “warp” is sometimesused to mean traverse. The term “warp” means that the database platformprovides the ability for a user to be in/at any node level, any video orany audio, or within any industry, and transport the user to any othernode level, other video or other audio in another industry or the sameindustry. The hierarchically indexed multimedia database 206 enablesusers to warp, i.e., move, from one video or audio at one node level toanother video or audio at another node level, or from one node toanother node, without searching through the hierarchically indexedmultimedia database 206 manually, but instead by entering a descriptionof another video or audio or node, in a field, next to the metadata ofthe current video or audio or node. Users can enter a node name, audioor video title, description or any unique identifier and the platformtransports them and brings up that new video or audio.

Referring to FIG. 2, the computer system performs (208) a scheduledprocess for automated pattern recognition triggers for self-building ofthe hierarchically indexed multimedia database 206. For example, in step210, the computer system performs node name text matching. The computersystem determines (212) whether keywords or phrases between two nodes inthe tree match each other. In some embodiments, the computer systemreferences a library of synonymous or equivalent words or named. Thehierarchy structure is stored in the database. As the user browses thehierarchy, the system returns the hierarchy to the user to view. Videosare associated to each hierarchy node and as the system returns thevideo metadata and multimedia content host's universal resource locator(URL) to be embedded in the web page, either as a thumbnail or anembedded, playable video. A URL, or web address, is a reference to a webresource that specifies its location on a computer network and amechanism for retrieving it. The multimedia content host is the same asor similar to the multimedia content host 528 illustrated and describedin more detail with reference to FIG. 5.

In some embodiments, the computer system extracts a first pattern from afirst node tree supported by a first branch of the hierarchicallyindexed multimedia database 206 using a machine learning module trainedbased on the hierarchically indexed multimedia database. The firstbranch is associated with a first industry. For example, in step 214,the computer system performs branch matching within the hierarchicallyindexed multimedia database 206. The computer system determines (216)whether there are surrounding nodes within a directly linked chain ofone or many nodes. Every video is associated to the issuer who uploadedthe video through a field in a video table which references the issuer'sPrimary Key.

In some embodiments, the computer system extracts a second pattern froma second node tree supported by a second branch of the hierarchicallyindexed multimedia database 206 using the machine learning module. Thesecond branch is associated with a second industry different from thefirst industry. The first node tree includes at least one node more thanthe second node tree. For example, the computer system performs (218)issuer/competitor matching within the hierarchically indexed multimediadatabase 206. The computer system determines (220) whether there aremultiple issuers associated with the same two nodes and whether the samecompetitors are associated to issuers at different nodes.

In some embodiments, the computer system determines that the firstpattern matches the second pattern using the machine learning module.The machine learning module is trained to compare two patterns extractedfrom the hierarchically indexed multimedia database 206. For example instep 222, the computer system matches (222) video metadata and performsspeech-to-text recognition on content stored in the hierarchicallyindexed multimedia database 206. The computer system determines (224)whether there are similarities between the content of the videosassociated to (or stored at) a first node and the videos associated to asecond node.

In some embodiments, the computer system cross-indexes at least one ofthe new node or the second node tree with the first node tree based onthe first pattern. For example in step 226, the computer systemdetermines whether any of the four steps 212, 216, 220, and 224 weresuccessful. If so, a cross-indexed node match is indicated. The computersystem verifies (238) review and approval for the cross-indexing. Thehierarchy is a node tree that exists on two planes, up/down andleft/right a single tree. It is possible to consider the variousindustries as a third dimension, with nodes being linked betweenindustries as well. This would be accomplished by using a cross-industryreference (cross-indexing) table to track the relationships betweennodes between different industries, or even between branches of the sameindustry if needed. The hierarchy is three dimensional and hascross-indexing between nodes at different industries.

The computer system determines (228) whether a first node has a siblingsecond node on the same level and not present elsewhere in thehierarchically indexed multimedia database 206. In some embodiments, aninvestor is enabled to search for and browse videos according to thehierarchy. The hierarchically indexed multimedia database 206 (referredto as an “issuerPixel database”) stores videos and tracks theirhierarchical categorization in a reference table which contains a recordof the VideoID (the Primary Key identifier for every video in the videotable) and the HierarchyNodeID (the Primary Key identifier for everynode in the industry categorization table). The reference table providesa single table which the system can search either based on the VideoIDor the HierarchyNodeID, and allows for multiple videos to be associatedto a single categorization node and for a single video to be associatedto multiple categorization nodes.

The computer system determines (230) whether a first node has a childnode not present under the other (second) node. In some embodiments,each categorization node in the hierarchy has an ID associated to it,and each video is associated to at least one hierarchy node. As a userviews a graphical user interface of the issuerPixel application (seeFIGS. 7-8) and browses or searches according to hierarchy, theissuerPixel application looks up the relevant hierarchy nodes and thensearches the reference table for those HierarchyNodeIDs. The systemdetermines the VideoIDs associated to those HierarchyNodeIDs and looksup the video's hosting address to begin the process of retrieving thevideo from a multimedia content host (e.g., YouTube in a particularimplementation), so that the user can view a thumbnail of the video andbegin playing the video if desired. The multimedia content host is thesame as or similar to the multimedia content host 528 illustrated anddescribed in more detail with reference to FIG. 5.

The computer system determines (232) whether the child nodes, siblingnodes, or ancestor nodes (parent, grandparent, great grandparent, etc.)are similar. In some embodiments, the hierarchy structure is stored inthe database so that as the user browses the hierarchy the systemreturns the hierarchy to the user to view. Videos are associated to eachhierarchy node and the system returns the video metadata and multimediacontent host's URL to be embedded in the web page (either as a thumbnailor an embedded, playable video).

The computer system determines (234) whether the nodes are in the sameindustry, in industries with cross-indexed nodes, or nodes generatedfrom matched patterns. In some embodiments, each video is associated tothe issuer that uploaded the video through a field in the video tablewhich references the issuer's Primary Key. Each video has an issuerassociated to it, and issuers cannot be deleted (they can beinactivated, but a record of them will remain in the database), so thatdata integrity is not lost. In addition to being associated to anissuer, the video is also associated directly to a company (here, anissuer user and an issuer company are differentiated).

In some embodiments, responsive to determining that the first patternmatches the second pattern, the computer system incorporates a new nodecorresponding to the at least one node within the second node tree inaccordance with the first pattern. For example, in step 236, thecomputer system analyzes the results of previous checks and potentiallyindicates a node addition (e.g., child or sibling node) to thehierarchically indexed multimedia database 206. An example of nodeaddition is illustrated and described in more detail with reference toFIG. 8.

The computer system reviews (202) the hierarchy of the hierarchicallyindexed multimedia database 206 and determines cross-indexed nodes. Instep 204, the computer system performs data entry for storage in thehierarchically indexed multimedia database 206. A new node is added tothe hierarchically indexed multimedia database 206. In some embodiments,the hierarchy is a node tree that exists on two planes: up/down andleft/right as a single tree. The various industries can be implementedas a third dimension, with nodes being linked between industries aswell. This is accomplished by using a “Cross-Industry Reference”cross-indexing table to track the relationships between nodes betweendifferent industries, or even between branches of the same industry ifneeded. The hierarchy is three-dimensional and performs cross-indexingbetween nodes of different industries and sometimes, within the sameindustry.

FIG. 3 is a flow diagram illustrating hierarchy building, in accordancewith one or more embodiments. The hierarchy building is performed for ahierarchically indexed multimedia database 206, illustrated anddescribed in more detail with reference to FIG. 2. In some embodiments,the process 300 of FIG. 3 is performed by a computer system, e.g., theexample computer system 1000 illustrated and described in more detailwith reference to FIG. 10. Particular entities, for example, thehierarchically indexed multimedia database 206 or a host service performsome or all of the steps of the process in other embodiments. Likewise,embodiments may include different and/or additional steps, or performthe steps in different orders. The host service is the same as orsimilar to the host service 524 illustrated and described in more detailwith reference to FIG. 5.

The computer system receives (302) a request to modify a node of thehierarchically indexed multimedia database 206 categorizing multipleissuer entities. The request is received from an investor entity or anissuer entity. The hierarchically indexed multimedia database 206includes at least one branch associated with a respective industry andsupports a node tree including the node to be potentially modified. Forexample, the computer system enables investors and issuers (companyusers) to provide requests in the hierarchy which are then implementedif approved. Users can submit requests to add, edit, or subtract nodesto the hierarchy through a suggestion box available for every visiblenode within the hierarchy view. In some embodiments for issuers, thehierarchy tree shows in the issuer profile questionnaire and in a mediaquestionnaire. The issuer profile questionnaire is the same as orsimilar to the issuer profile questionnaire 1100, illustrated anddescribed in more detail with reference to FIG. 11. The mediaquestionnaire is the same as or similar to the media questionnaire 1200illustrated and described in more detail with reference to FIG. 12. Forinvestors, the hierarchy tree shows in their homepage and videobrowsing/search pages, where they can browse through the hierarchy orsearch based on the hierarchy accordingly.

In some embodiments, the computer system extracts features indicative ofa priority of the request. A feature is an individual measurableproperty or characteristic of the raw input data. For example, featurescan be numeric or structural, such as strings or graphs. The featuresinclude a position of the node in the structure of the hierarchicallyindexed multimedia database 206. The features are extracted from therequest, other requests received to modify the node, and a structure ofthe hierarchically indexed multimedia database 206. For example in step304, the computer system analyzes the request to determine the priorityof the request based on the features using a machine learning moduletrained based on the structure of the hierarchically indexed multimediadatabase 206 and the other requests. The machine learning module is thesame as or similar to the machine learning module 518 illustrated anddescribed in more detail with reference to FIG. 5.

The computer system verifies (306) the request. In some embodiments, thecomputer system positions the request within the other requests based onthe priority. For example, the computer system weighs the value of therequest based on factors, such as who submitted the request (a list ofhigh weight email domain names is used to lend specific users moreweight with their requests), how many requests were submitted for thesame node (more weight for more requests for the same node), and theposition of the node in the hierarchy, as well as others. A requesthaving a higher weight shows higher in the administrator panel's list tobe reviewed.

In some embodiments, once an administrator approves of a request, thesuggested change is automatically implemented by the computer system andthe user is notified of the change. Changes include adding a node underthe target node, removing a target node and all descendants of thatnode, and changing the name of a node. An administrator can review eachchange before it is implemented, and may conditionally accept a changeof a node so that the user is notified the change was accepted. Thecomputer system transmits (308) a response to the investor entity or theissuer entity of the multiple issuer entities. The response indicatesthat the request to modify the node is satisfied.

In some embodiments, the computer system bypasses the request mechanismto directly modify a node based on prior, internal analysis (310) of thehierarchically indexed multimedia database 206. In some embodiments, thehierarchically indexed multimedia database 206 categorizes videosaccording to a proprietary industry classification tree. Additionalmetadata and files are also associated to each video (depending onuser-generated content) to provide users with the ability to search forand find videos using detailed fields such as the categorizationhierarchy and other qualitative characteristics based on the issuerprofile questionnaire and media questionnaire metadata. In someembodiments, audio media files have separate questionnaires with audiospecific fields and dropdowns. Additionally, the hierarchically indexedmultimedia database 206 application supports playing audio files using apodcast player. Audio files take the format of either MP3 or M4A files.MP3 (sometimes referred to as MPEG-1 Audio Layer III or MPEG-2 AudioLayer III) is a coding format for digital audio. MP4A (sometimesreferred to as MPEG-4 Part 3 or MPEG-4 Audio) is part of aninternational standard for audio coding. The hierarchically indexedmultimedia database 206 application can convert audio files from commonformats to MP3 or M4A.

In some embodiments, the computer system performs (316) automatedpattern recognition, using the machine learning module, triggered bychanges in the hierarchically indexed multimedia database 206. In someembodiments, more adaptive node pattern matching is implemented, e.g.,by matching patterns in node names or similarities in node names acrossbranch chunks. A parent node, direct child node, and direct grandchildnode (etc.) are called a branch. When two branches have significantnaming or description overlap with another industry, an automaticcross-index relationship between these branches is indicated. Branchescan be cross-indexed more than once. Branch sizes vary, with largermatching branches having a stronger “suggested correlation” than smallerbranches. In some embodiments, matching competitors in company profilesacross nodes/branches is performed. When two companies in differentnodes/industries have the same competitors, a relationship between thosenodes that could be weighed by an administrator is indicated. Using thesame weighting system as with weighting user requests, it is possible toweigh potential relationships within the hierarchies and theirrelevance.

In step 318, the computer system analyses (318) a textual name of thenode as well as metadata present within multimedia content saved at thenode, and performs data processing to identify an indicated change inthe node. In some embodiments, data elements are stored in thehierarchically indexed multimedia database 206 in a table designated forissuer profile questionnaire answers. Each answered issuer profilequestionnaire is given a unique ID and is represented by a single row inan issuer profile questionnaire table, with answers either in the formof alphanumeric input or as a Foreign Key reference to another table ofstored dropdown/checkbox inputs. For example, in a media questionnaire,the possible answers of “CEO,” “VP,” “COO,” etc., are all stored in aseparate “Presenter” table and each has a unique stored ID. In theissuer profile questionnaire table, for the column “Presenter” thevalues would all be Foreign Keys which reference a unique ID for one ofthe “Presenter” table values. When the computer system displays aPresenter, it will reference the issuer profile questionnaire table,pull the Foreign Key value in the presenter column, and then lookupwhich Presenter type matches that ID in the Presenter table.

The computer system reviews (320) the other requests received to modifythe node from users, investors, issuers, or the computer system itself(310). In some embodiments, requests are prioritized from some usersover others in accordance with who submitted the request (e.g., a listof high-weight email domain names will be used to lend specific usersmore weight with their requests), how many requests were submitted forthe same node (e.g., more weight for more requests for the same node),or a position of the node in the hierarchy (e.g., more weight for nodesin a higher position, since a change would affect more nodes). In someembodiments, requests are prioritized based on an age of the industry,an age of the node, a last modified date of the node, a number of parentnodes (of all replicas), a number of child nodes, a number of replicanodes, a number of recent requests for industry, a number of recentrequests for ancestor nodes, a number of companies associated to thisnode or descendant nodes, or a number of media files associated to thisnode or descendant nodes.

The computer system verifies (322) the node change. In some embodiments,the computer system positions the request within the other requestsbased on the priority. In some embodiments, the hierarchy supports up tonine category levels (from the highest level of “Industry” to the lowestof “Sub-Tier”), and is capable of supporting more. Authorized users areable to add, duplicate, subtract, and edit existing nodes as well as tocreate additional connections between existing nodes (both in the formof adding an association between nodes of adjacent levels in the sametree, and by cross-indexing nodes). Application users (both companyusers and investors) are able to submit requests to add, edit, andsubtract nodes to the hierarchy through a suggestion box available forevery node they can see within the hierarchy tree views allowed to them.For issuers, the hierarchy tree would show in the issuer profilequestionnaire and in the media questionnaire. For investors, thehierarchy tree would show in their homepage and video browsing/searchpages, where they can browse through the hierarchy or search based onthe hierarchy accordingly. The suggestion box is displayed in a fewdifferent ways, e.g., a button next to each hierarchy node which, whenclicked, shows a new popup window with the node's information (includingthe all of the direct ancestors of the node and the immediate childrennodes of that node) along with the three request types of Add, Remove,or Edit and a textbox for the user to explain their request. Requestswill be tied to the user's profile.

In some embodiments, the computer system modifies the node tree withrespect to the structure of the hierarchically indexed multimediadatabase 206, such that the request to modify the node is satisfied. Thehierarchically indexed multimedia database 206 is not only 1)automatically self-building and 2) not only being built by analysts.There is also an important 3) shared community building of thehierarchically indexed multimedia database 206. The shared communityconsists of users on the front end of the platform. Here with thehierarchically indexed multimedia database 206, users are runningqueries and analyzing the node tree of each industry hierarchy and theyhave the ability to press icons at each node level, to suggest to thesystem to add a node, delete a node, or replace a node with a new node.Therefore, the shared community is one of the ways that the databasecontinues to build and expand.

FIG. 4 is a flow diagram illustrating media upload, storage, and hostingfor a self-building hierarchically indexed multimedia database 206, inaccordance with one or more embodiments. The hierarchically indexedmultimedia database 206 is illustrated and described in more detail withreference to FIG. 2. In some embodiments, the process 400 of FIG. 4 isperformed by a computer system, e.g., the example computer system 1000illustrated and described in more detail with reference to FIG. 10.Particular entities, for example, the hierarchically indexed multimediadatabase 206, a multimedia content host or a host service perform someor all of the steps of the process 400 in other embodiments. Likewise,embodiments may include different and/or additional steps, or performthe steps in different orders. The host service is the same as orsimilar to the host service 524 illustrated and described in more detailwith reference to FIG. 5. The multimedia content host is the same as orsimilar to the multimedia content host 528 illustrated and described inmore detail with reference to FIG. 5.

The computer system receives (402) a media questionnaire and multimediacontent from a particular issuer entity of multiple issuer entitiescategorized by the hierarchically indexed multimedia database 206. Themedia questionnaire is the same as or similar to the media questionnaire1200, illustrated and described in more detail with reference to FIG.12. The hierarchically indexed multimedia database 206 is stored by amultimedia content host or host service. The hierarchically indexedmultimedia database 206 includes at least one node referencing theparticular issuer entity. For example, videos are categorized accordingto an industry classification tree. The system also associatesadditional metadata and files to each video, depending on user generatedcontent, to provide users with the ability to search for and find videosusing detailed fields, such as the categorization hierarchy and otherqualitative characteristics, e.g., based on issuer profile questionnairemetadata or media questionnaire metadata. The issuer profilequestionnaire is the same as or similar to the issuer profilequestionnaire 1100, illustrated and described in more detail withreference to FIG. 11.

An issuer completes a media questionnaire for each video uploaded to thesystem. The media questionnaire values entered by the user are stored inthe hierarchically indexed multimedia database 206 as a row of the mediaquestionnaire table. Data elements are stored in the hierarchicallyindexed multimedia database 206 in a table designated for mediaquestionnaire answers. Each answered media questionnaire is given aunique ID and is represented by a single row in the media questionnairetable, with answers either in the form of alphanumeric input or as aForeign Key reference to another table of stored dropdown/checkboxinputs. For example, in a media questionnaire, the possible answers of“CEO,” “VP,” “COO,” etc., are all stored in a separate “Presenter” tableand each have a unique stored ID. In the media questionnaire table, forthe column “Presenter” the values would all be Foreign Keys whichreference a unique ID for one of the “Presenter” table values. When thesystem needs to display who the Presenter was for this video, itreferences the media questionnaire table, pulls the Foreign Key value inthe presenter column, and then looks up which Presenter type matchesthat ID in the Presenter table.

In step 404, the computer system extracts media questionnaire metadataand metadata from the multimedia content, stores the metadata in thehierarchically indexed multimedia database 206. For example, thecomputer system extracts the metadata, using a machine learning module,from the media questionnaire and multimedia content, where the metadatais indicative of an industry. The machine learning module is the same asor similar to the machine learning module 518 illustrated and describedin more detail with reference to FIG. 5. The computer system identifiesa branch using the machine learning module based on the metadata. Insome embodiments, the computer system traverses the hierarchicallyindexed multimedia database 206 using a machine learning module, basedon the multimedia content, to identify at least one node correspondingto the issuer entity.

The computer system stores (408) the video or audio at a node tree(including the node) supported by the branch, such that the video oraudio is associated with the industry and the issuer entity. An addressof the multimedia content stored in the hierarchically indexedmultimedia database 206 is associated to the multimedia content. In someembodiments, videos are uploaded by a user from local storage afterfilling out a media questionnaire. The computer system verifies that thevideo is of an acceptable format and also verifies the integrity of thevideo using the ffmpeg command, which if specified to, reads the inputfile and reports any errors that appear. An example command line (forLinux) used is:

-   -   ffmpeg -v error -i file.avi -f null -2>error.log        Here, “-v error” refers to a certain level of verbosity (to show        some errors that are normally hidden because they don't affect        playability a much). A full error log with some generic        information about ffmpeg is output, which can be analyzed using        filters written to perform batch check of similar files.

The multimedia content is pushed (412) to remote storage at the hostservice. For example, the computer system stores videos and filesuploaded by users to the host service. The host service providesreliable, secure, and scalable data storage at a competitive price. Eachfile/video stored at the host service is assigned an address, and theservice database contains references linking the uploader and theaddress, along with a unique, system-assigned ID for the video (aPrimary Key). The database 206 also stores video metadata captured froma media questionnaire alongside a VideoID, such as the node(s) the videois associated to, the name of the video, the date the video was created,etc.

In step 410, the computer system performs address association for thevideo. In some embodiments, the computer system retains a copy of thevideo at the host service storage for backup/future reference. If thecomputer system is unable to retrieve the video from the 528, thecomputer system can either play the video directly (through a built-invideo player) or play the video through another backup hosting service.In some embodiments, once the computer system has determined whichvideos need to be retrieved based on a user's search criteria, thesystem looks up those video entries in the video table and then looks upthe multimedia content host (e.g., YouTube in a particularimplementation) URL associated to every video entry. This URL is pulledand then used in the front-end of the web application to embed thetarget video in the web app for viewing. The multimedia content host isthe same as or similar to the multimedia content host 528 illustratedand described in more detail with reference to FIG. 5.

The computer system analyzes (414) integrity of the multimedia contentstored at the host service. If the analysis fails (416), the issuerentity is notified (418) of the file integrity failure and the issuerentity is requested to resubmit the multimedia content. If the analysispasses, the multimedia content is pushed (420) to a multimedia contenthost, which can be the same as or different from the host service. Forexample, once the video is uploaded and stored at the host service, thecomputer system begins the process of automatically submitting the videoto a multimedia content host through the multimedia content host'sapplication programming interface (API). An API is a computing interfacethat defines interactions between multiple software or mixedhardware-software intermediaries. The host service automatically sendsthe multimedia content host the video file and metadata and, oncesuccessfully uploaded to the multimedia content host, the multimediacontent host returns a video URL. The host service stores this video URLwith a VideoID and references this URL whenever the application needs toretrieve and play the video. The video stored at the host service, theaddress generated, and the database entries of the video address andmetadata are generated simultaneously by the database 206 at the time ofthe video submission by the user.

In some embodiments, the computer system receives a URL from themultimedia content host referencing the multimedia content stored at thenode. For example, in step 422, the host service or multimedia contenthost generates a URL corresponding to the multimedia content. Thecomputer system receives (424) the URL from the multimedia content hostfor the media embed, and the URL is stored in the hierarchically indexedmultimedia database 206 and associated to the media object. The computersystem can receive a combinatorial query from an investor entityrequesting the multimedia content. Responsive to receiving the query,the computer system displays the multimedia content using the URL on agraphical user interface.

FIG. 5 is a block diagram illustrating a system network architecture500, in accordance with one or more embodiments. The system networkarchitecture 500 includes users 502 (e.g., investor entities, issuerentities, and administrators), a cloud service 506, a multimedia contenthost 528, and analytics websites (534). Likewise, embodiments mayinclude different and/or additional components, or connected indifferent implementations. The system network architecture can beimplemented using components of the computer system 1000 illustrated anddescribed in more detail with reference to FIG. 10.

The cloud service 506 provides an on-demand cloud computing platform andAPIs, e.g., on a metered pay-as-you-go basis. The cloud service providesa variety of abstract technical infrastructure and distributed computingbuilding blocks and tools. The cloud service 506 includes a Domain NameSystem (DNS) resolver 504, an application load balancer 508, a container510, an instance 512, an analytics module 514, an instance 516, amachine learning module 518, an instance 520, a hierarchy managementtool 522, a host service 524, and remote host storage 526. DNS refers toa hierarchical and decentralized naming system for computers, services,or other resources connected to the Internet or a private network. TheDNS resolver 504 is a server on the Internet that converts domain namesinto Internet Protocol (IP) addresses. IP refers to the principalcommunications protocol in the Internet protocol suite for relayingdatagrams across network boundaries.

In a particular implementation, the hierarchically indexed multimediadatabase (referred to as an “issuerPixel database”) stores all videosand files uploaded by users to Amazon S3 storage under an issuerPixelAmazon Web Services (AWS) account. S3 refers to Amazon Simple StorageService, a service offered by Amazon Web Services that provides objectstorage through a web service interface. S3 storage provides reliable,secure, and scalable data storage at a competitive price. Eachfile/video stored in S3 is assigned an address, and the issuerPixeldatabase contains references linking the uploader and this address,along with a unique, system-assigned ID for that video (a Primary Key).Once the video is uploaded and stored in S3, the computer system willthen begin the process of automatically submitting the video to theissuerPixel database's multimedia content host 528 (e.g., YouTube in aparticular implementation) through the multimedia content host 528'sAPI. The issuerPixel application will automatically send the multimediacontent host 528 the video file and the metadata, and once successfullyuploaded to the multimedia content host 528's issuerPixel account, themultimedia content host 528 will return the video URL. The issuerPixelapplication will then store this video URL with the VideoID and willreference this URL whenever the application needs to retrieve and playthe video.

The computer system retains the copy of the video in S3 storage forbackup/future reference, so if the system is unable to retrieve thevideo from the multimedia content host 528 (e.g., YouTube in aparticular implementation), the computer system can either play thevideo directly (through a built-in video player) or play the videothrough another backup multimedia content host service (e.g., Vimeo,Panopto, Vidyard, etc., in particular implementations). The issuerPixelapplication stores all of the data collected via the issuer profile andmedia questionnaires, user feedback, and user activity on the website inthe database in the form of tables. The issuer profile questionnaire isthe same as or similar to the issuer profile questionnaire 1100,illustrated and described in more detail with reference to FIG. 11. Themedia questionnaire is the same as or similar to the media questionnaire1200, illustrated and described in more detail with reference to FIG.12.

The issuerPixel application uses a relational database (e.g., MySQL in aparticular implementation), which is structured to maintain dataintegrity and to inherently support the relationships between objectssuch as users and questionnaire responses. MySQL refers to anopen-source relational database management system built using StructuredQuery Language (SQL). The hierarchically indexed multimedia database ishosted using Amazon's Relational Database Service (RDS) service underthe issuerPixel AWS account in a particular implementation. Amazon RDSrefers to a distributed relational database service by Amazon WebServices. It is a web service running “in the cloud” designed tosimplify the setup, operation, and scaling of a relational database foruse in applications. Videos are stored in Amazon's S3 storage servicewith each video being assigned an address by S3. This address is storedin the hierarchically indexed multimedia database and associated to theuploader as well as the video metadata (hierarchical categorization,date, name of video, etc.). The video being stored in S3, the addressbeing generated, and the hierarchically indexed multimedia databaseentries of the video address and metadata are all generatedsimultaneously by the issuerPixel application at the time of the videosubmission by the user. The application load balancer 508 automaticallydistributes incoming traffic across multiple targets, such as AmazonElastic Compute Cloud (EC2) instances, containers, and IP addresses, inone or more availability zones. Amazon EC2 is a part of Amazon'scloud-computing platform, Amazon Web Services.

The container 510 is administered by a scalable container managementservice that isolates the container 510 from others and bundles itssoftware, libraries and configuration files. The container 510 isisolated from other containers and bundles its own software, libraries,and configuration files. The container 510 communicates with othercontainers through well-defined channels.

The instances 512, 516, and 520 are each instances of the applicationfor users 502 to operate a hierarchically indexed multimedia database,such that each instance is a provisionable entity, and a combination ofIT resource instance (target connectivity and connector configuration)and resource object (provisioning mechanism). The hierarchically indexedmultimedia database is the same as or similar to the hierarchicallyindexed multimedia database 206 illustrated and described in more detailwith reference to FIG. 2.

The analytics module 514 provides financial analytics and views. In someembodiments, the computer system uses the analytics module 514 todetermine a first metric quantifying social media engagement,communication network activity, a trading volume, and a stock valueassociated with a particular issuer entity. The social media engagementincludes at least one of a social sentiment API feed or a socialsentiment indicator. The social media engagement measured includes, butis not limited to, Facebook, Instagram, Pinterest, Twitter, Wechat,Ozone, Tumblr, Messenger, Reddit, SnapChat, Line, TikTok, etc. Socialsentiment sites and platforms include sites such as Stock Twits, etc.The social sentiment API feeds include Social Sentiment.io, SocialMarket Analytics, and Hedge Chatter. Messenger Apps include WhatsApp,Viber, Telegram, and Facebook Messenger. The communication networkactivity includes at least one of instant messaging activity, instantmessaging frequency, or a chat room population. The trading volume, anda stock value can be obtained from the analytics websites 534. In someembodiments, the computer system mines the Internet to aggregate changesin the social media engagement, the communication network activity, thetrading volume, and the stock value associated with the particularissuer entity. Mining refers to a process of discovering patterns inlarge data sets and across the Internet using searches, machinelearning, statistics, and database systems. Determining the first metricis based on the changes. In some embodiments, determining the secondmetric includes triangulating between the social media engagement, thecommunication network activity, the trading volume, and the stock valueassociated with the first issuer entity.

The machine learning module 518 encapsulates a specific machine learningalgorithm, function, or code library that builds a model based on sampledata, known as “training data,” in order to make predictions ordecisions without being explicitly programmed to do so. The machinelearning module 518 applies machine learning techniques to generate amachine learning model that, when applied to extracted features, outputsindications of whether the features have an associated property. As partof the generation of the machine learning model, the machine learningmodule 518 forms a training set of features by identifying a positivetraining set of features that have been determined to have the propertyin question, and, in some embodiments, forms a negative training set offeatures that lack the property in question. In one embodiment, themachine learning module 518 applies dimensionality reduction (e.g., vialinear discriminant analysis (LDA), principle component analysis (PCA),or the like) to reduce the amount of data in the features for contentitems to a smaller, more representative set of data.

The machine learning module 518 uses supervised machine learning totrain the machine learning model, with the features of the positivetraining set and the negative training set serving as the inputs.Different machine learning techniques—such as linear support vectormachine (linear SVM), boosting for other algorithms (e.g., AdaBoost),neural networks, logistic regression, naïve Bayes, memory-basedlearning, random forests, bagged trees, decision trees, boosted trees,or boosted stumps—may be used in different embodiments. The machinelearning model, when applied to the features extracted, outputs anindication of whether the features have the property in question.

In some embodiments, the computer system mines the Internet formultimedia content associated with multiple industries using the machinelearning model 518. The machine learning model 518 is trained usingfeatures indicative of at least a particular industry of the multipleindustries. The multiple industries are categorized by thehierarchically indexed multimedia database, which includes multiplebranches including a particular branch associated with the particularindustry.

In some embodiments, the computer system uses the machine learning model518 to cluster the multimedia content among multiple issuer entities ofthe particular industry using deep learning. Deep learning architecturesinclude deep neural networks, deep belief networks, recurrent neuralnetworks, and convolutional neural networks. Deep learning involves theuse of multiple layers in the network having an unbounded number oflayers of bounded size, which permits practical application andoptimized implementation. In some embodiments, the deep learning isconfigured to determine a relationship from the multimedia contentbetween each issuer entity of the multiple issuer entities and eachother issuer entity of the multiple issuer entities.

In some embodiments, the hierarchically indexed multimedia databaseautomatically populates the same video or audio of the same company atthe same node level with the same name under a different industry in thehierarchy. The hierarchically indexed multimedia database automaticallypopulates multiple companies and their attached videos and audiorecordings located at multiple node levels within and across multipleindustries. The hierarchically indexed multimedia database is constantlyscanning the industry hierarchies looking for patterns of identicalpaths of multiple node levels in one industry to copy the missing nodelevels with associated companies and their corresponding audio andvideos to another industry. In this way, the database is bothcross-indexing and automatically building itself.

The hierarchy management tool 522 adds nodes to node trees, replicatesportions of node trees, instantiates branches, updates node trees, andcross-indexes nodes based on new multimedia content and information fromthe analytics websites 534. In some embodiments, rule sets are definedfor use in hierarchy management. For example, an issuer is associatedwith at least one node, determined in the issuer profile questionnaire.An issuer can select a higher tier node to associate to its profile,because a company can provide multiple services and products. An issuercan have multiple nodes associated to it, both within the same industryand across industries. To select multiple nodes, the issuer can use ahierarchy dropdown selection and choose to add a node to its profile.Issuer signups are reviewed by the computer system before they areapproved in the platform; optionally, mandatory review-alerts aretriggered if multiple nodes are selected by a company. The nodesassociated to video/audio files can be different from what is associatedto an industry but is a direct descendant of one of the company'sprofile nodes. A Video/Audio node is the lowest node level in a branch.

In some embodiments, a rule set is used to associate Video/Audio nodesto a maximum of three nodes, and they can be associated to across-indexed node. These are lowest tier nodes (see FIG. 8). Analternative to this is to allow a video/audio file to be associated tomultiple nodes, but they are lowest-level nodes under the company node.Cross-indexed nodes are considered functionally equivalent, meaning if avideo is associated to node cross-indexed to another, then searchingeither node should show the same video. All nodes are consideredproducts for the purposes of categorization, but in the mediaquestionnaire and in the metadata associated to each video, the computersystem will differentiate between the media files as eitherproduct-related, service-related, or both.

In some embodiments, the computer system uses the hierarchy managementtool 522 to generate a node tree structured in accordance with therelationship between each issuer entity and each other issuer entity.Each node of the node tree is associated with a respective issuerentity. The hierarchy management tool 522 incorporates the node treewithin the hierarchically indexed multimedia database, such that thenode tree is supported by the particular branch. In some embodiments,the computer system determines that video or audio stored on theparticular branch of the hierarchically indexed multimedia databasemismatches the particular industry. The hierarchy management tool 522transfers the video or audio to a second branch of the hierarchicallyindexed multimedia database, wherein the video or audio matches a secondindustry associated with the second branch.

The cloud service 506 includes the host service 524, which refers to aWeb-based or cloud-based hosting service that provides object storageusing a scalable storage infrastructure through a Web service interface.Objects, which allow for uses such as storage for Internet applications,backup and recovery, disaster recovery, data archives, data lakes foranalytics, and hybrid cloud storage can be stored.

The host storage 526 refers to a distributed relational database servicerunning in the cloud for setup, operation, and scaling of thehierarchically indexed multimedia database for use in applications. Insome embodiments, videos and metadata are kept private throughconfiguring the host's storage and retrieval account settings. Forexample, in a particular implementation, YouTube has three video listingsettings: Public, Private, and Unlisted. Public allows all users inYouTube to search for the video and see the video based on the videoname/metadata. Private prevents any user except the owner and up tofifty authorized YouTube users from viewing the video or seeing thevideo in search results. Unlisted prevents users from seeing the videoin search results, but allows users to view the video if they have theURL of the video. The Unlisted setting is sometimes used for each of thevideos. Thus, the proprietary categorization information is only storedand available through the hierarchically indexed multimedia database,and no meta data besides the video name is passed to the multimediacontent host 528. Thus, none of the proprietary categorization hierarchydata would be passed to the multimedia content host 528.

The multimedia content host 528 refers to an online video platform thatenables users to view, download, upload, share videos, or live streamvideos using the Internet. The multimedia content host 528 includes avideo display service 530 and an analytics engine 532. The video displayservice 530 provides playback, interactive video tools, and acustomizable player. In some embodiments, a home page provides videosand audio and links to videos (videos and list formats) and audio, suchas news-related videos and audio of the day, industry videos and audioof the day, sector videos and sector audio of the day, group videos andaudio of the day, videos and audio of a specific node level, producttype, or service type, volume-related videos of the day, volume-relatedaudio of the day, price-related videos of the day, price-related audioof the day, high-viewing activity videos, high listening activityaudios, growth in rate of viewing activity of videos, or growth in rateof listening activity of audio.

The embodiments that are disclosed herein in connection with video applyas well to audio, for example, podcasts. Video and audio (podcasts,webcasts, management conference calls, webinars, etc.,) content is allsubject to categorization within the hierarchy, video and audio tradingalerts, video and audio thumbnails, video and audio cross indexing,video and audio correlated trading analysis, video and audio (podcast)trading alerts, and a self-building hierarchy for video and audio.

The analytics engine 532 provides built-in video viewing analytics ofwho is watching the videos, or listening to the audios, such asconference calls/podcasts (e.g., analysts, individual investors,portfolio managers, CEOs, C-level executives, competitors, recruiters,or vendors and suppliers to the company within an industry, sector,subsector, node level). The analytics engine 532 determines what isbeing watched/listened to (e.g., company video or audio, industry, suchas aerospace, robotics, sector video or audio, subsector video or audio,or video or audio at node levels). The analytics engine 532 determineshow many separate viewers or listeners are watching the videos orlistening to the audio (e.g., by company, industry, sector, subsector,or node levels).

In some embodiments, the analytics engine 532 determines for how longthey are watching it or listening to it (e.g., seconds, minutes, hours,days, weeks, months, or years). The analytics engine 532 determines howmany times have they watched the same video or listened to the sameaudio about a particular company or a specific node level. The analyticsengine 532 determines growth in number of viewers/listeners watching itor listening to it (e.g., last 15 minutes, 30 minutes, 45 minutes, lasthour, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 hours, etc., last 24 hours, 1-30days, month, quarter, week-to-date, month-to-date, year-to-date, or last12 months). The analytics engine 532 determines how many times a userwatched videos or audio about a particular industry, sector, subsector,or a node level. In some embodiments, video viewing activity (viewingmetrics) and audio listening activity (audio metrics) is automaticallyrecorded and correlated, including: who is watching/listening, what arethey watching or listening to, how many viewers are watchingit/listening to it, how long are they watching or listening, or how manytimes they are watching it or listening to it, at any node level.

In some embodiments, the computer system uses the analytics engine 532to determine a second metric quantifying user engagement with multimediacontent stored at a node of the hierarchically indexed multimediadatabase. The multimedia content and the node are each associated withthe particular issuer entity of the multiple issuer entities, where thehierarchically indexed multimedia database categorizes the multipleissuer entities. In some embodiments, the computer system determines amultidimensional correlation of the first metric to the second metric.For example, the viewing and listening metrics are correlated to asecurity price change (stock/bond/any type of security), a tradingvolume/change in volume by security, a security price and volume changesof all public companies at a specific node level, securityperformance/change in performance of a composite (index/ETF etc.,)resulting from change in price performance of underlying companies thatmake up that index or ETF, etc., resulting from video viewing activityor audio listening activity, changes in money flow into/out of anindustry, sector, group, sub-sector, or a node level, group and sectorrotation, e.g., out of In-Vitro Fertility companies and into In VivoFertility, companies trend analysis, increase in funding of privatecompanies currently conducting a financing that have posted videos andor audio to the platform and the correlation between number ofviews/viewers or listens/listeners to the progress of the privatecompany's funding campaign. An exchange traded fund (ETF) is a type ofsecurity that tracks an index, sector, commodity, or other asset, butwhich can be purchased or sold on a stock exchange the same as a regularstock.

In some embodiments, video viewing and audio listening on the platformis determined based upon a multiplicity of metrics, which can be amountof video viewers/audio listeners, growth in viewers/listeners, and canalso be time-based metrics with respect to video views and audiolistens, and measuring these video viewing and audio listening metricsat the industry, sector, group, node level or company level. The systemthen simultaneously receives, warehouses, and measures engagement fromthe various social media platforms, sentiment from the social sentimentweb sites and platforms, and messaging metrics, such as chat activity,chat frequency, growth in chat room populations.

The computer system ranks the particular issuer entity among themultiple issuer entities based on the multidimensional correlation. Thecomputer system uses the hierarchy management tool 522 to update thenode to include data describing a rank of the particular issuer entityamong the multiple issuer entities based on the ranking. In someembodiments, the computer system displays a graphical user interfacerepresenting the multi-dimensional correlation (e.g., a bar chart ofvideo watchers and audio listeners, a line chart of video watchers andaudio listeners, regression analysis with two standard error bandsacross multiple time periods, or trend analysis by day, week, month,quarter, or year-to-date). In some embodiments, the computer systemdisplays a graphical user interface representing histograms, scattergrams, pie charts, flow charts, binary tree charts, time lines, or areacharts showing the multi-dimensional correlation.

In some embodiments, change in video and audio metrics are compared,which is measured at any node level, and which could be at the companylevel or industry, sector, sub-sector, group or any node level includingcompany level, to the change in the social media engagement, e.g.,Twitter or Facebook, and social sentiment indicators, e.g., Stocktwits,and messenger activity of a specific stock or general stock chat group,frequency change in chatter and other messenger metrics of the messengerapps, e.g., WhatsApp, Viber, etc. The computer system providesbi-directional and multi-directional correlations of theviewing/listening activity of the platform to the social mediaengagement, social sentiment indicators, and messenger app activity.Measurements and correlations are one to one, one to many, and many tomany.

The system has a large selection of alerts that can be set based uponquantitative levels of views/listens, social engagements, socialsentiment indicators, messenger activity and their correlations. Thesocial media or social sentiment or messenger activity may be sourceddirectly from these sources or via an API, or from the company/issuer'ssocial media or social sentiment or messenger app account which theyhave provided to us to access. For example, “Notification Alert: SendAlert to User via email and SMS/text when: Video Views or Audio Listensof XYZ Company increase by 100 within the last eight hours and socialengagements,” e.g., likes, increase by 1000 in the last twelve hours,positive social sentiment increases by 30% within the last four days,and messenger chatter activity in stock chat room increases by 50 peoplein the last week.

The analytics websites 534 refer to entities that provide stockanalysis, financial analysis, brokerage recommendations, and bond creditratings. In some embodiments, the computer system mines the analyticswebsites 534, using the machine learning module 518, to identify achange in a rating of the particular issuer entity. The rating isprovided by the analytics websites 534 for multiple issuer entities. Thecomputer system transmits the multimedia content and the change in therating of the particular issuer entity to the multimedia content host528 for storage at the identified node.

In some embodiments, trading, viewing, and listening alerts are createdby setting alerts on parameters, e.g., who is watching the video orlistening to the audio (competitor company, wall street analyst), whattype of video are they watching, or audio are they listening to (e.g.,company presentation, video about Food and Drug Administration (FDA)approval, video re proxy 14A, 14F), watching video or listening to audioat company level, industry level, sector level, node level, how manyviewers are watching it/listeners are listening to it, how long(duration) are they watching or listening, how many times they arewatching it or listening to it (company video, videos or company audioor multiple audios), security price change (stock, bond, any type ofsecurity), trading volume/change in volume by security, security priceand volume changes of all public companies at a specific node level, orsecurity performance/change in performance of a composite (index/ETF,etc.) resulting from change in price performance of underlying companiesthat make up that index or ETF, etc., resulting from video viewingactivity or resulting from audio listening activity. FDA refers to thefederal agency under the U.S. Department of Health and Human Services.

In some embodiments, trading, viewing, and listening alerts are createdby setting alerts on parameters, e.g., changes in money flow into/out ofan industry, sector, group, sub-sector, node level, group and sectorrotation, e.g., out of In-Vitro Fertility companies and into In VivoFertility companies, correlation coefficient based alert, e.g.,correlation coefficient of 0.95 of two public companies within twostandard error bands over a six month period (e.g., a geometric increasein views of a video or listens of an audio of one company versus anothercompany at the same node level or one node level above or below couldtrigger a pairs trade with one of the pair long and the other pairshort, all precipitated from video views of the pairs), changes inviewing and listening activity as it relates to company and industrynews, or technical analysis indicators correlated to viewing andlistening activity and vice versa, e.g., moving averages, stochastics,etc.

In some embodiments, natural language processing, e.g., linguistics,computer science, and artificial intelligence and facialrecognition/emotion recognition from the video or audio is used topredict confidence or lack thereof in company representations (e.g.,operating results, earnings, cash flow, revenue, etc.), productlaunching on time, likelihood of closing a merger, management successionor lack thereof, confidence of completing a financing or lack thereof. Aclassification system for issuers and an alert system is provided byembodiments based on the issuer profile questionnaire used to enter anissuer's data into the hierarchically indexed multimedia database.

FIG. 6 is a flow diagram illustrating an example process for servingmedia on user query flow, in accordance with one or more embodiments.The media is organized in a hierarchically indexed multimedia database.The hierarchically indexed multimedia database is the same as or similarto the hierarchically indexed multimedia database 206, illustrated anddescribed in more detail with reference to FIG. 2. In some embodiments,the process 600 of FIG. 6 is performed by a computer system, e.g., theexample computer system 1000 illustrated and described in more detailwith reference to FIG. 10. Particular entities, for example, thehierarchically indexed multimedia database or a host service performsome or all of the steps of the process in other embodiments. Likewise,embodiments may include different and/or additional steps, or performthe steps in different orders. The host service is the same as orsimilar to the host service 524 illustrated and described in more detailwith reference to FIG. 5.

The computer system receives (602) a combinatorial query from aninvestor entity that is searching for multimedia content based on text.In some embodiments, the computer system receives a combinatorial queryfrom an issuer entity, referencing interactions with the multimediacontent stored at a node. For example, the query is combinatorial toallow search on any and all parameters at once, as well as additionalparameters that can include (when recently posted, last day, week,month, quarter, this calendar year, or date range) or price. In someembodiments, a data feed is provided for price change versus previousday, price performance for day, week, month, quarter, YTD, or marketcap.

The computer system performs (604) a node search for node names inbranches of the hierarchically indexed multimedia database. For example,a user search consists of text and dropdown selections, and each of thedropdown selections are used to narrow the results of the search and thetext input is used to search across video names and descriptions. Asvideos are found which match the various criteria, their URLs andmetadata are retrieved and shown to the user. The multimedia contenthost URLs and remote storage addresses are stored in the video tablealong with other video-specific information. When the system looks up avideo, it also has access to these addresses. The multimedia contenthost is the same as or similar to the multimedia content host 528illustrated and described in more detail with reference to FIG. 5.

The computer system performs (606) a video search for video titles anddescriptions. In some embodiments, the investor-facing front endincludes multiple text boxes joined together by Boolean operators toallow users to search for videos, audio files, or issuers based onAND/OR conditions. These text boxes act as search terms in addition tothe dropdown selectors available in the screener tool. In someembodiments, the hierarchically indexed multimedia database providesfront end users with the ability to search for companies and nodes thathave identical strategic partners and overlapping or identical keysuppliers/vendors, and overlapping or identical founders by company andnode level, and provide the names and percentage overlapping oridentical strategic partners and key suppliers/vendors and founders.Users also have the ability to run searches by correlation coefficient(in decimals or percentages) of overlapping or identical key partnersand key vendors/suppliers and overlapping or identical founders.

The computer system performs (608) an issuer search for company namesand competitor names. In some embodiments, during issuer signup, adatabase entry in the issuer table is populated along with all of theprofile data associated with that company (e.g., name, company type,ticker symbol, etc.). Each company has a unique, system generated ID(Primary Key) associated with it, which is also used as a Foreign Key inthe video table so that each video is associated with a company. Whenthe computer system retrieves company/video data to show to the user, itcan also lookup and retrieve the company profile data using thesereference IDs (Primary/Foreign Keys) to see the relationships betweenentries in each table.

The computer system identifies (610) result nodes that fit the searchcriteria as well as cross-indexed nodes. In some embodiments when noresult node is found, the computer system uses a hierarchy managementtool to instantiates a new node in the hierarchically indexed multimediadatabase based on the combinatorial query. The new node is associatedwith an issuer entity. The hierarchy management tool stores themultimedia content at the new node. The hierarchy management tool is thesame as or similar to the hierarchy management tool 522 illustrated anddescribed in more detail with reference to FIG. 5.

The computer system receives (612) an industry query from an investorsearch based on industrial categorization, hierarchy, and otherqualitive fields of the hierarchically indexed multimedia database.Investor users can search for and browse videos according to thehierarchy (industry categorization nodes) and video metadata (title,company, description, etc.). The computer system stores videos andtracks their hierarchical categorization in the reference table, whichcontains a record of the Video's ID (the Primary Key identifier forevery video in the video table) and the HierarchyNodeID (the Primary Keyidentifier for every node in the industry categorization table). FIG. 6illustrates an example of how the computer system returns mediafiles/companies based on investor text searches or by browsing thecategorization hierarchies.

The computer system retrieves (614) child nodes of the hierarchicallyindexed multimedia database as the investor selects an industry and asublevel of the hierarchically indexed multimedia database. In someembodiments, the computer system analyzes individual user usage of thehierarchically indexed multimedia database application. For issuers, thesystem analyzes data, such as video/podcast uploads, mediaquestionnaires, issuer profile questionnaires, node associations, anduser engagement to provide recommendations to improve issuerexperience/success. The issuer profile questionnaire is the same as orsimilar to the issuer profile questionnaire 1100, illustrated anddescribed in more detail with reference to FIG. 11. The mediaquestionnaire is the same as or similar to the media questionnaire 1200,illustrated and described in more detail with reference to FIG. 12.

Recommendations can be displayed on a graphical user interface using aprofile strength bar/meter showing the relative strength and/orcompletion of a profile, as well as the strength of the questionnairesand other uploaded files. Additional metrics used to incentivizefollowing recommendations include issuer profile view counts,miscellaneous file view counts, product link view counts, searchappearances for an issuer and for each of the issuer's uploaded mediafiles. Investor recommendations include recommendations for profilecompletion, recommended issuers, industries, or issuer content based ona user's interests and previous activity (e.g., search history,viewing/listening history, file access history, or product linkhistory).

The computer system retrieves (616) media and companies associated tonodes as each level is selected. The media is filtered based onqualitative criteria selected by the investor. In some embodiments, thehierarchically indexed multimedia database (referred to as an“issuerPixel database”) returns data for security price changes, tradingvolume/changes, or security price and volume changes, and providesreports showing correlations and comparisons between these sets of data.Data sources for these include services such as finnhub.io andtiingo.com, which provides detailed trading information to recognizefinancial movement patterns as they relate to the IssuerPixelapplication's video and audio usage.

The computer system performs (618) a cross-index check to determinewhether a result node is cross-indexed to another node. Videos from bothnodes are displayed by a video display module, e.g., the video displaymodule 530, illustrated and described in more detail with reference toFIG. 5. For example, the reference table is searched by the computersystem based on the Video ID or the HierarchyNodeID, and enablesmultiple videos to be associated to a single categorization node and fora single video to be associated to multiple categorization nodes.Examples of these two cases are provided below, where video 5673 occurstwice in the table and is associated to two different nodes, indicatingthat the video has been categorized under two distinct categorizations,either within the same Industry or in separate industries. Additionally,hierarchy node 243 occurs twice in the table, indicating that twoseparate videos (46 and 5673) are both associated with this node.

VideoID HierarchyNodeID ReferenceID (Foreign Key) (Foreign Key) 51 46243 52 5673 427 53 3432 139 54 5673 243

Each video is associated with an issuer who uploaded the video through afield in the video table that references the issuer's Primary Key. Eachvideo has an issuer associated to it. Issuers are not deleted (they canbe inactivated, but a record of them remain in the database), such thatthe system never loses data integrity. In addition to being associatedwith an issuer, the video is also associated directly to a company.

The computer system retrieves (620) a URL provided by the multimediacontent host. The multimedia content host is the same as or similar tothe multimedia content host 528 illustrated and described in more detailwith reference to FIG. 5. The URL is associated to media objectsassociated to the result nodes. Once the system has determined whichvideos need to be retrieved based on a user's search criteria, thesystem looks up those video entries in the video table and then looks upthe multimedia content host's URL associated to every video entry. ThisURL is pulled and then used in the front-end of the web application toembed the target video in the web app for viewing.

The computer system embeds (622) a media file thumbnail and a title inthe results provided. For example, when the URL is pulled from thedatabase, the URL is sent to the Web application where the softwareretrieves the video from the URL and embeds the video in the web pagefor viewing. The call made to retrieve the video from the URL mayrequire sending credentials to the host service in order to access thevideo. If a user clicks on a specific media file, the computer systemretrieves the media file metadata.

The computer system displays (626) a file details page, the media fileusing an embedded media file player, the metadata, and company data,etc. The media may be displayed using the video display module 503,illustrated and described in more detail with reference to FIG. 5. Forexample, the computer system displays a change in a rating of an issuerentity on a graphical user interface in response to a query referencinga result node. In another example, responsive to receiving acombinatorial query from an investor entity referencing a node tree, thecomputer system transmits a multidimensional correlation to the investorentity. The multidimensional correlation is described in more detailwith reference to FIG. 5. In some embodiments, responsive to receiving acombinatorial query from an investor entity referencing a particularindustry, the computer system displays a graphical user interfacedisplaying a node tree associated with the industry to the investorentity.

In some embodiments, the computer system transmits investor activity toan issuer entity responsive to receiving a combinatorial query. Forexample, an analytics module can determine investor activity viewingmultimedia content. The analytics module is the same as or similar tothe analytics module 514 illustrated and described in more detail withreference to FIG. 5. The analytics module aggregates interactions of theinvestor entity with the multimedia content stored at the node intoinvestor activity formatted in accordance with the structure of thehierarchically indexed multimedia database. In some embodiments, thecomputer system receives a combinatorial query from an investor entityreferencing a node. The computer system transmits a rank of an issuerentity associated with the node, among the multiple issuer entities, tothe investor entity in response to the combinatorial query. Rankingmultiple issuer entities is described in more detail with reference toFIG. 5.

In some embodiments, the computer system analyzes and compares videosand audio against each other with respect to characteristics, such asviews or listens. For example, the computer system can compare mediausing screening (screener page) characteristics including industrytaxonomy-related, company characteristics, media type (e.g., vlog,etc.), reporting status, or research coverage type. The media can becompared based on the media questionnaire characteristics, social mediacharacteristics, fundamentals (ratios and operating metrics), technicals(stock technical analysis), duration, subject matter (video/audiosubject), or meta data. In some embodiments, a machine learning moduleis used to compare the media using association rule learning, arule-based machine learning method for discovering relationships betweenvariables in large databases. For example, the machine learning moduleuses rule-based machine learning to identify a set of relational rulesthat collectively represent the knowledge captured by the database. Therule-based machine learning approach includes learning classifiersystems, association rule learning, and artificial immune systems.

FIG. 7 is a diagram illustrating an example graphical user interfacedisplaying a hierarchy dashboard 700 for a self-building hierarchicallyindexed multimedia database, in accordance with one or more embodiments.The hierarchy dashboard 700 is presented in the form of the graphicaluser interface to an investor entity or an issuer entity. Thehierarchically indexed multimedia database is the same as or similar tothe hierarchically indexed multimedia database 206 illustrated anddescribed in more detail with reference to FIG. 2.

In some embodiments, a computer system receives video or audio from anissuer entity. The computer system is the same as or similar to theexample computer system 1000 illustrated and described in more detailwith reference to FIG. 10. The computer system determines that theissuer entity belongs to an industry excluded from the multipleindustries of the self-building hierarchically indexed multimediadatabase. The determining is performed using a machine learning modulebased on the video or audio. The computer system generates a new branchassociated with the excluded industry. The computer system incorporatesthe new branch within the hierarchically indexed multimedia database asshown in FIG. 7. For example, the computer system generates a new nodeassociated with the issuer entity and supported by the new branch. Thecomputer system stores the video or audio at the new node. A suggestionbox to edit nodes in the hierarchy can be displayed in different ways.In some embodiments a button is displayed next to each hierarchy nodewhich, when clicked, shows a new popup window with the node'sinformation (including direct ancestors of the node and the immediatechild nodes of that node) along with the three request types of Add,Remove, or Edit and a textbox for the user to explain their request (seeFIG. 7). Requests are tied to the user's profile.

In some embodiments, the industry hierarchies shown in FIG. 7 are usedas a mechanism to create lead generation for sales of products andservices of the issuer and further investment community visibility forthe issuer, via the front end user, who is an investor or prospectiveproduct/service purchaser, who has direct access to the issuer via theplatform's industry hierarchies, for both sales of products and servicesof the issuer and further investment community visibility for the issueron a per transaction-per click basis. For example, an issuer profilequestionnaire is modified to include a URL for the company'sproduct/service sales department, email address for sales department,phone number for product/service sales/business development department,URL for a company's investor relations department, email address forcompany's investor relations contact, phone number for company'sinvestor relations contact, or company credit information. The issuerprofile questionnaire is the same as or similar to the issuer profilequestionnaire 1100, illustrated and described in more detail withreference to FIG. 11.

In some embodiments, front-end users use industry hierarchies not onlyfor investment research, but also for a company'sproduct/service-related sales, via lead generation for the sales of itsproducts/services (sales leads for the issuer) and providing directaccess for investors to the issuer, providing the issuer with investmentcommunity visibility to the issuer. At any node level an investor/usercan click a button to go to a company's URL for product/service sales, acompany's email address, or call the company.

The investment community is also provided with a visibility-pay perclick feature. At any node level, an investor/user can click a button togo to a URL for an issuer's investor relations department, an e-mailaddress for the company's investor relations contact, a phone number forthe company's investor relations contact, etc. Each action transmitsremuneration to the hierarchically indexed multimedia database 206 on aper-click basis, emanating from the hierarchy at the lowest node level.For example, a front-end user clicks on a “Flight Management Systems”node. The main flight management systems manufacturers (HoneywellInternational Inc. (U.S.), Thales Group (France), General ElectricCompany (U.S.), Leonardo-Finmeccanica S.p.A (Italy), Rockwell Collins(U.S.), Esterline Technologies (U.S.), and Garmin Ltd.) are listed. Theuser then clicks on any one of these companies. The user then has achoice. They can click on Videos or Audio. They can instead opt for: 1.Contact product sales; or 2. Contact investor relations. Then, they canselect any one of the above “Click Actions.” Each action charges theissuer on a per-click basis and initiates the selected action.

Referring to FIG. 7, the hierarchically indexed multimedia databaseapplication provides the following features. “Node Warp”: a button nextto a node or set of “Industry” level search results where the user canwarp to a related node as suggested by the application (oradministrators). This feature is useful in nodes that can fall withinmultiple industries (cross-indexed nodes or otherwise similar nodes).For example, if an issuer associated to a node in Medical Devices entersthe Healthcare industry tree, and then goes to Medical Services→MedicalServices (non-transport)→Transplant Surgery→Heart Transplant→MedicalDevices, the application displays a button that allows that user to goto Medical Devices pertaining to Heart Transplants, within the MedicalDevice Industry tree. “Cross Indexing Notes for nodes”: This featureallows administrators to add free-text notes to nodes. This is primarilyintended to track cross-indexed nodes, but may also be used for othernote taking purposes. “Search Text Box on Back End”: to search nodes byname or ID. “Tracking and Logging for Duration for Issuer to go ThroughQuestionnaire”: The application will detect when the user lands on apage and when a form is submitted. Additionally, user input is trackedin fields even before the form is submitted. This allows the capture ofmore information from the user, such as user fall-off, partially inputinformation, and general user flow through the issuer profilequestionnaire. Greater insight into user behavior allows the issuerprofile questionnaire to be adjusted in terms of content, layout andformat so as to improve user retention. Additionally, the partiallycompleted forms may be automatically saved when users leave or close thepage so that the form is restored when users return to the same form.This improves a media questionnaire completion and video upload rates.The media questionnaire is the same as or similar to the mediaquestionnaire 1200, illustrated and described in more detail withreference to FIG. 12.

In some embodiments, the hierarchically indexed multimedia databaseapplication provides the following features. “Duration Logging forResearch Analysts Viewing Industry Hierarchy Pages”: the applicationwill periodically ping the browser to see if that page is still open,and will display a log of the duration spent on each industry page byuser and timestamp. This feature, in addition to the logging of eachnode's creation by user and timestamp, will provide administrators withgreater insight into hierarchy completion rates, problematic industryhierarchies, analyst efficiency and strengths, and rates of researchimprovement. “Allowing Administrators to Add and Remove ‘Test’ Companiesto the System”: for verification, the application is tested by addingcompanies to the system by going through the registration process in thedevelopment environment, including the issuer profile questionnaire forissuers and media questionnaires for media files. This will allow testsand ensure the web application is functioning as expected, as well asassuring the quality of the user experience for issuers.

In some embodiments, the hierarchically indexed multimedia databaseapplication provides the following features. “Front End—Boolean LogicSearch”: in addition to drop downs for front end user's searching ofvideo and audio files. The investor-facing front end will includemultiple text boxes joined together by Boolean operators to allow usersto search for videos, audio files, or issuers based on AND/ORconditions. These text boxes may act as search terms in addition to thedropdown selectors available in the screener tool. “Issuer—User privatechat functionality with issuer (‘Issuer Online’)”: the application willinclude a real-time communication protocol between issuers and users (achat feature). This communication protocol will mirror other commoncustomer support chat protocols. The chat protocol will be an optionalfeature for issuers which will need to be activated either during issuerregistration or in the issuer's preferences.

On the investor-facing front end, each registered front end user hasaccess to communicate with the issuer through the chat popup window whenthey see/are notified “Issuer Online.” Users can use this chat featureto ask questions and communicate privately with the representative ofthe issuer. This would create more investment community visibility forissuers so issuers can gather more prospective investors/users bycreating more direct contact with them. Users can also search forcompanies where issuers representatives are online and available tochat. Additional methods to display to/notify users that issuers areonline include adding a section on the home page highlightingindustries/sectors where issuer representatives are online; and“Companies” section (on home page) where issuer representatives areonline. This feature is considered a stretch goal post-launch.

In some embodiments, the hierarchically indexed multimedia databaseapplication provides the following features. “Analysis-BasedSuggestions”: an automated requests feature for issuers, using marketdata analysis and/or application data analysis to suggest what kind ofcontent to post, metadata to add to existing content, and videocharacteristics/qualities to improve on. Requests may be made directlythrough the issuer dashboard or through email notifications, theserequests may be directed en-masse to issuers based on platform wide dataanalysis. “Recommendation Engine”: individually tailored recommendationsfor additional metadata, video files, and audio files based on system oradministrator analysis for issuers and investor front-end users. Theapplication will analyze individual user usage of the application. Forissuers, the system will analyze data such as video/podcast uploads,media questionnaires, issuer profile questionnaires, node associations,and user engagement to provide recommendations to improve issuerexperience/success. One method of displaying this recommendation isthrough a Profile strength bar/meter showing the relative strengthand/or completion of their profile, as well as the strength of theirissuer profile questionnaire and other uploaded files. Additionalmetrics to incentivize following recommendations include issuer profileview counts, file view counts, product link view counts, searchappearances for the issuer and for each of the issuer's uploaded mediafiles. Investor recommendations may include recommendations for profilecompletion, or recommended issuers, industries, or issuer content basedon that user's interests and previous activity (e.g., search history,viewing/listening history, file access history, product link history).

In some embodiments, the hierarchically indexed multimedia databaseapplication provides the following features. “Smart Searches WhichIgnore ‘Clutter’ Terms”: searches that include words like“company”/“companies” or other common phrases like “LLC” shouldaccurately provide results without cluttering results with othercompanies that have the word “company” in the title. Some possiblesolutions to this issue include either fully ignoring these common wordsor performing two searches and merging their results, or performing asearch with these exact results but ignoring other results based onthese “clutter” words. “Utilize Natural Language Processing”: theapplication will analyze the direct message traffic between issuers andinvestors via the chat feature. The application (or administrators)could then make requests to issuers about new video and audio files topost based on interest/need shown in chats. Investors will also be ableto suggest to issuers the types of videos that they would like to see.This would encourage issuers to post more video and audio files to theplatform.

In some embodiments, the hierarchically indexed multimedia databaseapplication provides the following features. “News Blog and RSS feeds”:the platform (referred to as an “issuerPixel platform”) includes a blogcontaining articles/posts written by authorized users and the siteadministrators. These blog posts improve site SEO and provide more valuecontent for users of the site. In addition to adding content for sitevisitors, the aspect promotes features within the site or issuers whopay for such promotions (sponsored articles/promotions). In addition tothe posts or articles generated by the issuerPixel application, the sitecontains an RSS feed pulling articles and/or news from other investorinstitutions and sources. This feed is curated by administrators so asto provide value relevant to issuers on platform and current markettrends/investor interests. “Virtual Assistant”: the issuerPixelapplication features a virtual assistant to facilitate communicationwith support tickets and sales appointments. This assistant integrateswith the issuerPixel database's external CRM system to better organizecustomer interactions. “Provide Videography Company Suggestions”: forissuers to facilitate easier and higher quality video production. Theplatform integrates with an advertising firm (on a commission basis) toget videography companies to be advertised on the recommendedvideographers list. This provides the benefits of: more videos fromissuers around the globe, ad revenue from videography companies aroundthe globe (without polluting the front end of the platform), improvedmedia quality on platform, and additional awareness from variousvideography/audio production contacts.

In some embodiments, the hierarchically indexed multimedia databaseapplication provides the following features. “Front end users have theability to suggest [via sending a submission to the researchadministrator just like with nodes] adding a company to a node level, atany node level, within any industry”: users can request adding a company(issuer) at any node level. The issuer profile questionnaire includesadditions to support this feature include Strategic Partners, KeySuppliers/Vendors, Company Founders, Company Executives. The databasetakes the strategic partner and key supplier/vendor and founder data andcompares the data between all companies in all industries and at allnode levels, seeking overlapping (identical) partners and identical keysuppliers/vendors and founder(s). The database provides front end userswith the ability to search for companies and nodes that have identicalstrategic partners, overlapping or identical key suppliers/vendors, andoverlapping or identical founders by Company and node level, and providethe names and percentage overlapping or identical strategic partners,key suppliers/vendors, and founders. Users also have the ability to runsearches by correlation coefficient (in decimals or percentages) ofoverlapping or identical key partners, key vendors/suppliers, andfounders.

In some embodiments, the hierarchically indexed multimedia databaseapplication provides issuers with the ability to add transcript fileswhich the system will process using text-to-speech algorithms into audiofiles for user consumption. Issuers choose whether or not to convert thetranscript to audio upon upload, and if they choose to do so the issuerthen fills out the media questionnaire for the transcript file. The webapplication mines the SEC and other data sources for transcript files tobe added to the database for companies. These files may be convertedinto audio files depending on whether it is deemed appropriate to do so.

FIG. 8 is a diagram illustrating an example graphical user interfacedisplaying a portion 800 of a hierarchy of a self-buildinghierarchically indexed multimedia database, in accordance with one ormore embodiments. The hierarchically indexed multimedia database is thesame as or similar to the hierarchically indexed multimedia database 206illustrated and described in more detail with reference to FIG. 2. Theportion 800 of the hierarchy is displayed on a graphical user interfaceof the hierarchically indexed multimedia database application.

In some embodiments, the hierarchy tree includes nineteen levels(including “Industry”), and can be expanded to include more levels. Eachlevel includes one or more nodes, which in turn include a description, astatus, and parent/child relationships. Nodes can have one or morechildren, and also have a parent (except “Industry” nodes) but can bereplicated under more than one parent. Child nodes can be created underany node except the bottom-most layer, and can be associated to anyparent node on the same level. Parent-child relationships can also becreated or removed at any time between existing adjacent layer nodes.Each replicated node is considered a new, unique node in that it has itsown unique ID and can be altered independent of other versions of thesame replicated node.

In some embodiments, the graphical user interface of the hierarchicallyindexed multimedia database application drives the followingfunctionality. “Expand/Collapse Node's Children”: clicking this buttonwill expand or collapse the immediate children nodes of this node.“Cross-Indexed Nodes” (including icon): allows the user to cross-index anode with another node in a separate industry. This creates arelationship between two nodes which the computer system uses tounderstand that the two nodes can be treated as equivalent andinterchangeable, so that issuers and media files associated to onecross-linked node would also appear as being associated to thetwin-linked node. Cross-Indexed nodes are marked with a chain-linkingicon on the node in the hierarchy tree. Users can view which nodes aselected node is linked to in the cross-indexed details, and may addmore than one cross-indexed node association at once. “Description”:node values consist of text input, which is automatically formatted sothat the first letter is always capitalized, and all followingcharacters keep the case formatting as input. Node values are unique tothat level, meaning if a duplicate node value is entered in the samelevel as an existing node, then that node will not be created and theuser will be notified.

In some embodiments, the graphical user interface of the hierarchicallyindexed multimedia database application drives the followingfunctionality. “Note”: allows users to add admin-only viewable andeditable text. The purpose of this note field is to track cross-indexednode requests from the research analyst team, and can be used forgeneral note taking purposes as well. Nodes with note fields have acomment bubble icon in their node visible on the hierarchy tree screen.“Status”: nodes can be marked as active/inactive; inactive nodes arehidden from non-administrative users and will eventually be deleted.Industry trees can be activated or inactivated; inactive trees are movedto a separate view to prevent clutter. Administrative users with theappropriate permissions can change the status at will at any time.“Delete Node”: delete node allows users to permanently delete a node;doing so also deletes all nodes underneath the target node. “Move”:allows user to move target node and all sub-nodes to becoming a childnode destination node which is selected through a search dropdown (usingName and ID). “Duplicate as Industry”: allows user to duplicate targetednode to a new industry, so that the target node becomes an industrylevel node and all child-nodes are moved along with the node to theirappropriate new levels (the sub-tree structure is maintained duringmove). “Copy Child Nodes”: allows user to copy any selection of a targetnode's child nodes to be pasted underneath a destination parent node.This does not remove the original child nodes. “Update”: allows user tosave any changes made to target node (e.g., description change), statuschange, associated parent/child nodes. “Select All” (parent/child nodecheckbox selection): allows the user to select all child or all parentnodes.

In some embodiments, the graphical user interface of the hierarchicallyindexed multimedia database application drives the followingfunctionality. “Mass Node Create” (separated by semi colon): clicking an“Add <Industry Level>” button will allow users to add a node at thatlevel, while selecting one or more parent nodes. Users can createmultiple nodes at once by separating each new node with a semicolon.“Expand/Collapse All”: allows users to expand or collapse all nodes inthe industry hierarchy tree at once. “Portrait/Landscape”: allows usersto change the orientation of the industry hierarchy tree from top downto left right and back again. “Refresh”: allows the user to refresh thepage so as to show the latest changes in the industry tree.“Save/Restore Backup”: industry node trees can be individually saved (upto ten saves per tree) and a saved version of a tree can be loaded atany time. Loading a save will overwrite existing tree data.

In some embodiments, the graphical user interface of the hierarchicallyindexed multimedia database application drives the followingfunctionality. “Generate PDF”: generates a PDF version of the industrynode tree showing all nodes of the tree. The displayed tree will matchthe current orientation of the industry tree. “Zoom In/Out”: allows theuser to zoom in or out on the industry hierarchy tree without effectingthe rest of the page. “Search Nodes”: allows the user to search all ofthe nodes within the current industry tree by description and ID. Thisfeature will display a list of result nodes and allow the user to selecta result to jump to that node in the tree. “Automated DB Backup”: systemautomatically backs up the entire DB on a weekly basis and stores thesnapshots where a developer can restore it if needed. This requiresdirect database access to restore a previous version. “Node ChangeLogging”: node creation is tracked according to who made the node andthe timestamp of the node creation. “Analyst Time Spent on HierarchyView”: the system tracks the amount of time an analyst user has spentviewing a specific webpage according to when they started viewing thepage and when they stopped. This is tracked by having the pageperiodically ping the server for as long as the page is open, so theserver logs when the page was initially opened and for how long it wasopen. This log is visible in the administrative portal to users with theappropriate privileges. “Load Optimization”: the system has beenoptimized to load up to 40K nodes in a single hierarchy tree withoutcrashing a web browser.

In some embodiments, the graphical user interface of the hierarchicallyindexed multimedia database application drives the followingfunctionality. “Checkbox”: adding a checkbox next to each node in thehierarchy tree, with administrative users able to check multiple nodesat once and then select an action at the top of the tree to apply thataction to the entire selection of nodes at once. Actions include Delete,Move, and Copy. Another button for “clear selection” will remove anyselected nodes. “Add Company to Nodes”: administrators will be able toadd companies directly to nodes with some subset of metadata to uniquelyidentify the company. Administrators can view what companies are in thesystem and what nodes they are associated to, as well as what companiesare associated to each node in the node details of the hierarchy tree.Issuers will be able to view their existing company profile on signupand be able to choose to “claim” that company during the signup process.“Drag and Click Nodes”: administrators can click a node and drag it toanother place within the same hierarchy tree. The administrator canconnect and disconnect nodes together (in terms of parent/childrelationships). The computer system can also detect if a connectionshould exist if a user drags a node on top of a line connecting anexisting parent/child pair and insert itself as an additional node inbetween the parent and child, separating parent and child nodes whileforming new relationships. The existing parent node becomes a parentnode of the selected node, and the existing child node becomes a childof the selected node. Users can choose to click and drag an entiresubtree. Users can connect subtrees back to the hierarchy tree bydrawing a parent/child relationship between the top of the subtree andany desired parent node. This effectively “moves” the subtree underneatha target node location.

An example process of the self-building executes as follows. AFertilization node exists under the Healthcare Industry but there arenot any nodes below it in Healthcare. An issuer entity or user can enterthe Medical Device industry hierarchy (see FIG. 7) and generate, orsubmit for approval, two nodes below Fertilization consisting of InVitro Fertilization and In Vivo Fertilization. The entries are approvedby the computer system and the hierarchically indexed multimediadatabase then performs the follow two actions automatically. First, thehierarchically indexed multimedia database adds the two nodes to theMedical Device industry hierarchy. Second, the hierarchically indexedmultimedia database automatically recognizes the path in the Healthcareindustry hierarchy and creates the two nodes under Fertilization of InVitro Fertilization and In Vivo Fertilization.

In some embodiments, after the system makes the change, it alerts thecomputer system to perform verification. There are, thus, three methodsfor hierarchy building: automatic self-building hierarchy, serviceadministration, and user requests, which can occur from one or moreusers simultaneously on the platform. For example, an issuer loads avideo for their company under: Healthcare→OutpatientFacilities→Fertility Clinics→In Vivo Fertilization→Company. The databaseautomatically populates the same video of the same company at the samenode level with the same name called In Vivo Fertilization under adifferent industry in the hierarchy; in this case, the medical deviceindustry. Thus unsupervised cross-indexing of videos throughout thehierarchies is performed. There are some industries where a company isproviding a service and a product or there is a product that isfacilitating a service by them or others. In this example, the serviceis under Healthcare and the product is under Medical Device. The featureis significant in solving the technical problem of unsupervisedcross-indexing.

FIG. 9 is a flow diagram illustrating an example process forself-building hierarchically indexed multimedia databases, in accordancewith one or more embodiments. The hierarchically indexed multimediadatabase is the same as or similar to the hierarchically indexedmultimedia database 206, illustrated and described in more detail withreference to FIG. 2. In some embodiments, the process of FIG. 9 isperformed by a computer system, e.g., the example computer system 1000illustrated and described in more detail with reference to FIG. 10.Particular entities, for example, the hierarchically indexed multimediadatabase or a host service perform some or all of the steps of theprocess in other embodiments. Likewise, embodiments may includedifferent and/or additional steps, or perform the steps in differentorders. The host service is the same as or similar to the host service524 illustrated and described in more detail with reference to FIG. 5.

The computer system traverses (904) the hierarchically indexedmultimedia database, which includes multiple branches categorizingmultiple industries. Each branch of the hierarchically indexedmultimedia database supports at least one node tree associated with atleast one issuer entity and stores multimedia content associated withthe at least one issuer entity. In some embodiments, a group of a parentnode, direct child node, and direct grandchild node is called a branch.When two branches have significant naming or description overlap withanother industry, a cross-index relationship is indicated between thesebranches. Branches can be cross-indexed more than once. Branch sizes mayvary, with larger matching branches having a stronger suggestedcorrelation than smaller branches.

The computer system extracts (908) a first pattern from a first nodetree supported by a first branch of the hierarchically indexedmultimedia database, using a machine learning module, trained based onthe hierarchically indexed multimedia database. The machine learningmodule is the same as or similar to the machine learning module 518illustrated and described in more detail with reference to FIG. 5. Thefirst branch is associated with a first industry. Using automatedself-building, the hierarchically indexed multimedia database (referredto as an “issuerPixel database”) automatically detects patterns withinthe node trees and determines whether patterns match closely enough tosuggest replicating additional nodes from one pattern to the other.

The computer system extracts (912) a second pattern from a second nodetree supported by a second branch of the hierarchically indexedmultimedia database using the machine learning module. The second branchis associated with a second industry different from the first industry.The first node tree includes at least one node more than the second nodetree. The machine learning module is the same as or similar to themachine learning module 518 illustrated and described in more detailwith reference to FIG. 5. The machine learning module encapsulates aspecific machine learning algorithm, function, or code library thatbuilds a model based on sample data, known as “training data,” in orderto make predictions or decisions without being explicitly programmed todo so. The machine learning module 518 applies machine learningtechniques to generate a machine learning model that, when applied toextracted features, outputs indications of whether the features have anassociated property.

The computer system determines (916) that the first pattern matches thesecond pattern using the machine learning module. The machine learningmodule is trained to compare two patterns extracted from thehierarchically indexed multimedia database. For example, For example, ifBranch A (a chain of directly related nodes) contains four nodes, andthree of the node names closely match (e.g., greater than 98% wordsimilarity) three nodes of the same relationship pattern in anotherindustry's Branch B, the computer system would then indicate adding thefourth node from A to B in the same position. The computer system cansearch for patterns using multiple weighted criteria, which can beadjusted. Other criteria which may be used to evaluate and determine ifnodes should be added are whether two industries share manycross-indexed nodes, or if the system has previously suggested addingnodes from one to the other and those requests were accepted by anadministrator.

Responsive to determining that the first pattern matches the secondpattern, the computer system incorporates (920) a new node correspondingto the at least one node within the second node tree in accordance withthe first pattern. In addition to the above methods of building thehierarchy, the issuerPixel application also allows investors and issuers(company users) to provide requests in the hierarchy that are thenimplemented. Users are able to submit requests to add, edit, andsubtract nodes to the hierarchy through a suggestion box available forevery node they can see within the hierarchy tree views allowed to them.For issuers, the hierarchy tree would show in an issuer profilequestionnaire and in a media questionnaire. The issuer profilequestionnaire is the same as or similar to the issuer profilequestionnaire 1100, illustrated and described in more detail withreference to FIG. 11. The media questionnaire is the same as or similarto the media questionnaire 1200, illustrated and described in moredetail with reference to FIG. 12. For investors, the hierarchy treewould show in their homepage and video browsing/search pages, where theywould be able to browse through the hierarchy or search based on thehierarchy accordingly.

FIG. 10 is a block diagram illustrating an example computer system 1000,in accordance with one or more embodiments. In some embodiments, thecomputer system 1000 is a server computer, a client computer, a personalcomputer (PC), a user device, a tablet PC, a laptop computer, a personaldigital assistant (PDA), a cellular telephone, an iPhone, an iPad, aBlackberry, a processor, a telephone, a web appliance, a network router,switch or bridge, a console, a hand-held console, a (hand-held) gamingdevice, a music player, any portable, mobile, hand-held device, wearabledevice, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine.

In some embodiments, the computer system 1000 includes one or morecentral processing units (“processors”) 1005, memory 1010, input/outputdevices 1025, e.g., keyboard and pointing devices, touch devices,display devices, storage devices 1020, e.g., disk drives, and networkadapters 1030, e.g., network interfaces, that are connected to aninterconnect 1015. The interconnect 1015 is illustrated as anabstraction that represents any one or more separate physical buses,point to point connections, or both connected by appropriate bridges,adapters, or controllers. In some embodiments, the interconnect 1015includes, for example, a system bus, a Peripheral Component Interconnect(PCI) bus or PCI-Express bus, a HyperTransport or industry standardarchitecture (ISA) bus, a small computer system interface (SCSI) bus, auniversal serial bus (USB), an IIC (I2C) bus, or an Institute ofElectrical and Electronics Engineers (IEEE) standard 1394 bus, alsocalled Firewire.

In some embodiments, the memory 1010 and storage devices 1020 arecomputer-readable storage media that store instructions that implementat least portions of the various embodiments. In addition, in someembodiments, the data structures and message structures are stored ortransmitted via a data transmission medium, e.g., a signal on acommunications link. Various communications links can be used, e.g. theInternet, a local area network, a wide area network, or a point-to-pointdial-up connection. In some embodiments, the computer readable mediainclude computer-readable storage media, e.g., non-transitory media, andcomputer-readable transmission media.

In some embodiments, the instructions stored in memory 1010 areimplemented as software and/or firmware to program the processor 1005 tocarry out actions described above. In some embodiments, such software orfirmware are initially provided to the computer system 1000 bydownloading it from a remote system through the computer system 1000,e.g., via network adapter 1030. The various embodiments introducedherein can be implemented by, for example, programmable circuitry (e.g.,one or more microprocessors) programmed with software and/or firmware,or entirely in special purpose hardwired (non-programmable) circuitry,or in a combination of such forms. Special-purpose hardwired circuitrymay be in the form of, for example, one or more ASICs, PLDs, FPGAs, etc.

FIG. 11 is a diagram illustrating an example graphical user interfacedisplaying an issuer profile questionnaire 1100, in accordance with oneor more embodiments. The example issuer profile questionnaire 1100illustrated in FIG. 11 can include drop-down entries categorizing thecompany name, entity type—organizational structure, name of individualcompleting the questionnaire, e-mail address of individual completingthe questionnaire, URL for company's sales department, e-mail address ofcompany's sales department, phone number for product/servicesales/business development department, URL for company's investorrelations department, or e-mail address of company's investor relationscontact for an issuer entity. The example issuer profile questionnaire1100 can further include drop-down entries categorizing a phone numberfor company's investor relations contact, the company founders, CEO,strategic partnerships, public or private company type, base currency,legal entity type, public trading and reporting status, auditedfinancials, patents, or analyst rating for an issuer entity.

FIG. 12 is a diagram illustrating an example graphical user interfacedisplaying a media questionnaire 1200, in accordance with one or moreembodiments. The example media questionnaire 1200 illustrated in FIG. 12can include drop-down entries categorizing the industry, sector, subsector, group, sub group, echelon, sub echelon, tier, and sub tier for avideo upload. The example media questionnaire 1200 can further includedrop-down entries categorizing the video title, date of videopublication, video presenter, type of video, media sub type, subject ofcompany video, video description, recent road shows, investment bankhosted road show, name of investment bank, and top competitors for avideo upload.

In alternative embodiments, a computer system receives a request tomodify a node of a hierarchically indexed multimedia databasecategorizing multiple issuer entities. The request is received from aninvestor entity or an issuer entity of the multiple issuer entities. Thehierarchically indexed multimedia database includes at least one branchassociated with a respective industry and supports a node tree includingthe node. The computer system extracts features indicative of a priorityof the request. The features are extracted from the request, otherrequests received to modify the node, and a structure of thehierarchically indexed multimedia database. The computer systemdetermines the priority of the request based on the features using amachine learning module trained based on the structure of thehierarchically indexed multimedia database and the other requests. Thecomputer system partitions the request within the other requests basedon the priority. The computer system modifies the node tree with respectto the structure of the hierarchically indexed multimedia database, suchthat the request to modify the node is satisfied. The computer systemtransmits a response to the investor entity or the issuer entity of themultiple issuer entities. The response indicates that the request tomodify the node is satisfied.

In some embodiments, the features include a position of the node in thestructure of the hierarchically indexed multimedia database.

In some embodiments, a computer system receives issuer profilequestionnaires describing multiple issuer entities. The computer systemgenerates a hierarchically indexed multimedia database categorizing themultiple issuer entities based on the issuer profile questionnaires. Thehierarchically indexed multimedia database includes at least one branchassociated with a respective industry and supports at least one node.The at least one node references at least one issuer entity of themultiple issuer entities. The computer system extracts metadata using amachine learning module from multimedia content received from the atleast one issuer entity. The metadata is indicative of the respectiveindustry. The computer system identifies the at least one node using themachine learning module based on the metadata. The machine learningmodule is trained based on a structure of the hierarchically indexedmultimedia database. The computer system stores the multimedia contentat the at least one node, such that the multimedia content is associatedwith the respective industry and the at least one issuer entity. Thecomputer system aggregates interactions of at least one investor entitywith the multimedia content stored at the at least one node intoinvestor activity formatted in accordance with the structure of thehierarchically indexed multimedia database. The computer systemtransmits the investor activity to the at least one issuer entity.

In some embodiments, the computer system receives a combinatorial query,from the at least one issuer entity, referencing the interactions withthe multimedia content stored at the at least one node. Transmitting theinvestor activity to the at least one issuer entity is performedresponsive to receiving the combinatorial query.

In some embodiments, a computer system receives multimedia content froma particular issuer entity of multiple issuer entities categorized by ahierarchically indexed multimedia database stored by a multimediacontent host. The hierarchically indexed multimedia database includes atleast one node referencing the particular issuer entity. The computersystem mines analytics websites using a machine learning module toidentify a change in a rating of the particular issuer entity. Therating is provided by the analytics websites for the multiple issuerentities. The computer system traverses the hierarchically indexedmultimedia database using the machine learning module based on themultimedia content to identify the at least one node. The computersystem transmits the multimedia content and the change in the rating ofthe particular issuer entity to the multimedia content host for storageat the at least one node. The computer system receives a URL from themultimedia content host referencing the multimedia content and thechange in the rating stored at the at least one node. The computersystem receives a combinatorial query from an investor entity requestingthe multimedia content. Responsive to receiving the query, the computersystem displays the multimedia content and the change in the rating ofthe particular issuer entity using the URL on a graphical userinterface.

In some embodiments, a computer system determines a first metricquantifying user engagement with multimedia content stored at a node ofa hierarchically indexed multimedia database. The multimedia content andthe node are each associated with an issuer entity of multiple issuerentities. The hierarchically indexed multimedia database categorizes themultiple issuer entities. The computer system determines a second metricquantifying social media engagement, communication network activity, atrading volume, and a stock value associated with the issuer entity. Thecomputer system determines a multidimensional correlation of the firstmetric to the second metric. The computer system ranks the issuer entityamong the multiple issuer entities based on the multidimensionalcorrelation. The computer system updates the node to include datadescribing a rank of the issuer entity among the multiple issuerentities based on the ranking. The computer system receives acombinatorial query from an investor entity referencing the node. Thecomputer system transmits the rank of the issuer entity among themultiple issuer entities to the investor entity in response to thecombinatorial query.

In some embodiments, the computer system mines the Internet to aggregatechanges in the social media engagement, the communication networkactivity, the trading volume, and the stock value associated with thefirst issuer entity. Determining the second metric is based on thechanges.

In some embodiments, determining the second metric includestriangulating between the social media engagement, the communicationnetwork activity, the trading volume, and the stock value associatedwith the first issuer entity.

In some embodiments, the social media engagement includes at least oneof a social sentiment API feed or a social sentiment indicator.

In some embodiments, the communication network activity includes atleast one of instant messaging activity, instant messaging frequency, ora chat room population.

In some embodiments, prior to determining the first metric, the computersystem instantiates a node in the hierarchically indexed multimediadatabase based on the combinatorial query. The node is associated withthe issuer entity. The computer system stores the multimedia content atthe node.

In some embodiments, a computer system mines the Internet for multimediacontent associated with multiple industries using a machine learningmodel trained using features indicative of at least a particularindustry of the multiple industries. The multiple industries arecategorized by a hierarchically indexed multimedia database includingmultiple branches including a particular branch associated with theparticular industry. The computer system clusters the multimedia contentamong multiple issuer entities of the particular industry using deeplearning. The deep learning is configured to determine a relationshipfrom the multimedia content between each issuer entity of the multipleissuer entities and each other issuer entity of the multiple issuerentities. The computer system generates a node tree structured inaccordance with the relationship between each issuer entity of themultiple issuer entities and each other issuer entity of the multipleissuer entities. Each node of the node tree is associated with arespective issuer entity of the multiple issuer entities. The computersystem incorporates the node tree within the hierarchically indexedmultimedia database, such that the node tree is supported by theparticular branch. Responsive to receiving a combinatorial query from aninvestor entity referencing the particular industry, the computer systemgenerates a graphical user interface displaying the node tree to theinvestor entity.

In some embodiments, the particular branch is a first branch and theparticular industry is a first industry. The method further includesdetermining, by the computer system, that video or audio stored on thefirst branch of the hierarchically indexed multimedia databasemismatches the first industry. The computer system transfers the videoor audio to a second branch of the hierarchically indexed multimediadatabase. The video or audio matches a second industry associated withthe second branch.

The description and drawings herein are illustrative and are not to beconstrued as limiting. Numerous specific details are described toprovide a thorough understanding of the disclosure. However, in certaininstances, well-known details are not described in order to avoidobscuring the description. Further, various modifications may be madewithout deviating from the scope of the embodiments.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Certain terms that are used todescribe the disclosure are discussed above, or elsewhere in thespecification, to provide additional guidance to the practitionerregarding the description of the disclosure. For convenience, certainterms may be highlighted, for example using italics and/or quotationmarks. The use of highlighting has no influence on the scope and meaningof a term; the scope and meaning of a term is the same, in the samecontext, whether or not it is highlighted. It will be appreciated thatthe same thing can be said in more than one way. One will recognize that“memory” is one form of a “storage” and that the terms may on occasionbe used interchangeably.

Consequently, alternative language and synonyms may be used for any oneor more of the terms discussed herein, nor is any special significanceto be placed upon whether or not a term is elaborated or discussedherein. Synonyms for certain terms are provided. A recital of one ormore synonyms does not exclude the use of other synonyms. The use ofexamples anywhere in this specification including examples of any termdiscussed herein is illustrative only and is not intended to furtherlimit the scope and meaning of the disclosure or of any exemplifiedterm. Likewise, the disclosure is not limited to various embodimentsgiven in this specification.

It is to be understood that the embodiments and variations shown anddescribed herein are merely illustrative of the principles of 1 thisinvention and that various modifications may be implemented by thoseskilled in the art.

What is claimed is:
 1. A method comprising: mining, by a computersystem, the Internet for multimedia content associated with a pluralityof industries categorized by a hierarchically indexed multimediadatabase comprising a plurality of branches, each branch of theplurality of branches associated with a respective industry of theplurality of industries; clustering, by the computer system, themultimedia content among a plurality of issuer entities associated withthe hierarchically indexed multimedia database; determining, by thecomputer system, that the multimedia content is associated with anindustry and an issuer entity, the industry excluded from the pluralityof industries, the determining performed using machine learning based onthe multimedia content; generating, by the computer system, a branchassociated with the industry excluded from the plurality of industriesand a node associated with the issuer entity and supported by thebranch; storing, by the computer system, the multimedia content at thenode associated with the issuer entity; and modifying, by the computersystem, the hierarchically indexed multimedia database by incorporatingthe branch associated with the industry excluded from the plurality ofindustries within the hierarchically indexed multimedia database.
 2. Themethod of claim 1, wherein mining the Internet for the multimediacontent is performed using a machine learning model trained usingfeatures indicative of at least a particular industry of the pluralityof industries, the plurality of branches including a particular branchassociated with the particular industry.
 3. The method of claim 1,wherein the plurality of issuer entities comprises the issuer entity. 4.The method of claim 1, wherein clustering the multimedia contentcomprises determining, by the computer system, a relationship, from themultimedia content, between each issuer entity of the plurality ofissuer entities and each other issuer entity of the plurality of issuerentities.
 5. The method of claim 4, further comprising: generating, bythe computer system, a node tree structured in accordance with therelationship between each issuer entity of the plurality of issuerentities and each other issuer entity of the plurality of issuerentities, each node of the node tree associated with a respective issuerentity of the plurality of issuer entities; and incorporating, by thecomputer system, the node tree within the hierarchically indexedmultimedia database, such that the node tree is supported by the branchassociated with the industry excluded from the plurality of industries.6. The method of claim 1, further comprising generating, by the computersystem, a graphical user interface displaying the branch associated withthe industry excluded from the plurality of industries within thehierarchically indexed multimedia database in response to receiving acombinatorial query from an investor entity referencing the industry. 7.The method of claim 1, wherein the branch associated with the industryexcluded from the plurality of industries is a first branch and theindustry excluded from the plurality of industries is a first industry,the method further comprising: determining, by the computer system, thatvideo or audio stored on the first branch mismatches the first industry;and transferring, by the computer system, the video or audio to a secondbranch of the hierarchically indexed multimedia database, wherein thevideo or audio matches a second industry associated with the secondbranch.
 8. The method of claim 1, wherein the branch associated with theindustry excluded from the plurality of industries is a first branch andthe industry excluded from the plurality of industries is a firstindustry, the method further comprising: extracting, by the computersystem, a pattern from a second branch of the hierarchically indexedmultimedia database, the second branch associated with a second industryassociated with the first industry, wherein generating the first branchand the node associated with the issuer entity is performed based on thepattern.
 9. The method of claim 1, further comprising: receiving, by thecomputer system, issuer profile questionnaires describing a plurality ofissuer entities associated with the plurality of industries; andconstructing, by the computer system, the hierarchically indexedmultimedia database categorizing the plurality of industries based onthe issuer profile questionnaires.
 10. A computer system comprising: acomputer processor; and a non-transitory computer-readable storagemedium storing computer instructions, which when executed by thecomputer processor cause the computer system to: mine the Internet formultimedia content associated with a plurality of industries categorizedby a hierarchically indexed multimedia database comprising a pluralityof branches, each branch of the plurality of branches associated with arespective industry of the plurality of industries; determine that themultimedia content is associated with an industry and an issuer entity,the industry excluded from the plurality of industries, the determiningperformed using machine learning based on the multimedia content;generate a branch associated with the industry excluded from theplurality of industries and a node associated with the issuer entity andsupported by the branch; store the multimedia content at the nodeassociated with the issuer entity; and modify the hierarchically indexedmultimedia database by incorporating the branch associated with theindustry excluded from the plurality of industries within thehierarchically indexed multimedia database.
 11. The computer system ofclaim 10, wherein the computer instructions further cause the computersystem to cluster the multimedia content among a plurality of issuerentities associated with the hierarchically indexed multimedia database.12. The computer system of claim 11, wherein the plurality of issuerentities comprises the issuer entity.
 13. The computer system of claim11, wherein the computer instructions to cluster the multimedia contentcause the computer system to determine a relationship, from themultimedia content, between each issuer entity of the plurality ofissuer entities and each other issuer entity of the plurality of issuerentities.
 14. The computer system of claim 13, wherein the computerinstructions further cause the computer system to: generate a node treestructured in accordance with the relationship between each issuerentity of the plurality of issuer entities and each other issuer entityof the plurality of issuer entities, each node of the node treeassociated with a respective issuer entity of the plurality of issuerentities; and incorporate the node tree within the hierarchicallyindexed multimedia database, such that the node tree is supported by thebranch associated with the industry excluded from the plurality ofindustries.
 15. The computer system of claim 10, wherein mining theInternet for the multimedia content is performed using a machinelearning model trained using features indicative of at least aparticular industry of the plurality of industries, the plurality ofbranches including a particular branch associated with the particularindustry.
 16. The computer system of claim 10, wherein the computerinstructions further cause the computer system to generate a graphicaluser interface displaying the branch associated with the industryexcluded from the plurality of industries within the hierarchicallyindexed multimedia database in response to receiving a combinatorialquery from an investor entity referencing the industry.
 17. The computersystem of claim 10, wherein the branch associated with the industryexcluded from the plurality of industries is a first branch and theindustry excluded from the plurality of industries is a first industry,wherein the computer instructions further cause the computer system to:determine that video or audio stored on the first branch mismatches thefirst industry; and transfer the video or audio to a second branch ofthe hierarchically indexed multimedia database, wherein the video oraudio matches a second industry associated with the second branch. 18.The computer system of claim 10, wherein the branch associated with theindustry excluded from the plurality of industries is a first branch andthe industry excluded from the plurality of industries is a firstindustry, wherein the computer instructions further cause the computersystem to: extract a pattern from a second branch of the hierarchicallyindexed multimedia database, the second branch associated with a secondindustry associated with the first industry, wherein generating thefirst branch and the node associated with the issuer entity is performedbased on the pattern.
 19. The computer system of claim 10, wherein thecomputer instructions further cause the computer system to: receiveissuer profile questionnaires describing a plurality of issuer entitiesassociated with the plurality of industries; and construct thehierarchically indexed multimedia database categorizing the plurality ofindustries based on the issuer profile questionnaires.
 20. Anon-transitory computer-readable storage medium storing computerinstructions, which when executed by a computer processor cause thecomputer processor to: mine the Internet for multimedia contentassociated with a plurality of industries categorized by ahierarchically indexed multimedia database comprising a plurality ofbranches, each branch of the plurality of branches associated with arespective industry of the plurality of industries; determine that themultimedia content is associated with an industry and an issuer entity,the industry excluded from the plurality of industries, the determiningperformed using machine learning based on the multimedia content;generate a branch associated with the industry excluded from theplurality of industries and a node associated with the issuer entity andsupported by the branch; store the multimedia content at the nodeassociated with the issuer entity; and modify the hierarchically indexedmultimedia database by incorporating the branch associated with theindustry excluded from the plurality of industries within thehierarchically indexed multimedia database.